System

Abstract

An object of the system according to the embodiment is to make the work procedure of the local government efficient and to provide a unified manual.SOLUTION: A system includes a creation part, an efficiency part, a generation part, and a provision part. The creation unit creates a knowledge base. The streamlining unit streamlines the procedure based on the knowledge base created by the creation unit. The generation part automatically generates a manual on the basis of the procedure made efficient by the streamlining part. The provision unit provides a virtual window based on the manual generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, local government operations are highly personalized, and there is insufficient documentation of operational procedures and know-how, resulting in issues with uniformity and efficiency of responses.

[0005] The system according to the embodiment aims to streamline the business procedures of local governments and provide unified manuals. [Means for solving the problem]

[0006] A system according to an embodiment includes a creation unit, an efficiency improvement unit, a generation unit, and a provision unit. The creation unit creates a knowledge base. The efficiency improvement unit improves the efficiency of procedures based on the knowledge base created by the creation unit. The generation unit automatically generates a manual based on the procedures improved in efficiency by the efficiency improvement unit. The provision unit provides a virtual contact point based on the manual generated by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the business procedures of local governments and provide unified manuals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A knowledge base platform according to an embodiment of the present invention is a system that prevents local government operations from becoming personalized and promotes the creation of manuals for operational procedures and know-how. This system utilizes generative AI to create a knowledge base, optimize procedures, automatically generate manuals, and provide a virtual contact point. For example, the knowledge base platform consolidates manuals and handover documents from each department to create a unified knowledge base. Next, it monitors usage logs and business processes for internal systems and tools, automatically updating the knowledge base and optimizing procedures. Furthermore, it automatically generates manuals tailored to skill levels and creates training content using AI voice, providing up-to-date and appropriate human resource development content. Finally, it utilizes the knowledge base to provide a virtual contact point that directly responds to inquiries from residents. This enables the knowledge base platform to centrally manage information and provide optimal procedures, preventing the personalized use of know-how. Furthermore, using the knowledge base as a business support system or virtual contact point improves the quality of operations and reduces human resource resources through increased efficiency. This knowledge base platform can alleviate the human resource shortages faced by local governments and improve the convenience and satisfaction of citizen services.

[0029] A knowledge base platform according to an embodiment includes a creation unit, an efficiency improvement unit, a generation unit, and a provision unit. The creation unit creates a knowledge base. For example, the creation unit consolidates manuals and handover documents from each department to create a unified knowledge base. The efficiency improvement unit streamlines procedures based on the knowledge base created by the creation unit. For example, the efficiency improvement unit monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit automatically generates manuals based on the procedures streamlined by the efficiency improvement unit. For example, the generation unit automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit provides a virtual counter based on the manuals generated by the generation unit. For example, the provision unit provides a virtual counter that directly responds to inquiries from residents. This enables the knowledge base platform to create a knowledge base, streamline procedures, automatically generate manuals, and provide a virtual counter. As a result, the knowledge base platform according to an embodiment can alleviate the human resource shortage issues faced by local governments and improve the convenience and satisfaction of citizen services.

[0030] The creation unit can consolidate the manuals and handover documents of each department and create an integrated knowledge base. For example, the creation unit consolidates the manuals and handover documents of each department and creates a unified knowledge base. For example, the creation unit analyzes the business procedures and know-how of each department in detail and compiles them into a common format. By consolidating the manuals and handover documents of each department into a unified knowledge base, centralized management of information becomes possible. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the manuals and handover documents of each department into a generation AI and have the generation AI create a unified knowledge base.

[0031] The efficiency improvement department can monitor usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. For example, the efficiency improvement department can monitor usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. For example, the efficiency improvement department can monitor the progress and frequency of use of work in real time and review procedures as necessary. This makes it possible to automatically update the knowledge base and optimize procedures by monitoring usage logs and business processes of internal systems and tools. Some or all of the above-mentioned processing in the efficiency improvement department can be performed using, for example, AI, or without AI. For example, the efficiency improvement department can input usage logs of internal systems and tools into a generation AI and have the generation AI automatically update the knowledge base and optimize procedures.

[0032] The generation unit can automatically generate manuals tailored to skill levels and create training content using AI voice. The generation unit, for example, automatically generates manuals tailored to skill levels. For example, the generation unit automatically generates content tailored to skill levels, from basic manuals for new employees to advanced manuals for experienced employees. The generation unit also creates training content using AI voice. For example, the generation unit uses AI voice to create training content for employees. This makes it possible to automatically generate manuals tailored to skill levels and create training content using AI voice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to automatically generate a manual tailored to skill levels.

[0033] The providing unit can provide a virtual counter that directly responds to inquiries from residents. The providing unit, for example, provides a virtual counter that directly responds to inquiries from residents. For example, when a resident comes to the counter, the providing unit has an AI automatically respond and provide the necessary information. This provides a virtual counter that directly responds to inquiries from residents, thereby reducing the burden on staff. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may have a generating AI respond to inquiries from residents.

[0034] The creation unit can set priorities based on the importance of the work when analyzing the work procedures of each department in detail and compiling them into a common format. For example, the creation unit can set priorities based on the importance of the work when analyzing the work procedures of each department in detail and compiling them into a common format. For example, the creation unit can prioritize important work procedures and quickly add them to the knowledge base. The creation unit can also postpone less important work procedures, allowing for efficient resource allocation. The creation unit can also periodically review the importance of the work procedures and dynamically adjust the priorities. This enables efficient creation of a knowledge base by setting priorities based on the importance of the work. Some or all of the above-described processing in the creation unit can be performed using, for example, AI, or without AI. For example, the creation unit can input the importance of the work procedures into a generation AI and have the generation AI set the priorities.

[0035] The creation unit can select an efficient consolidation method by referring to past work history when consolidating manuals and handover documents for each department. For example, when consolidating manuals and handover documents for each department, the creation unit can select an efficient consolidation method by referring to past work history. For example, the creation unit analyzes past work history and prioritizes consolidating frequently used procedures. The creation unit can also identify procedures that frequently cause errors from the past work history and consolidate them by incorporating improvements. The creation unit can also optimize and consolidate work flows based on the past work history. This enables the creation of an efficient knowledge base by selecting the optimal consolidation method by referring to the past work history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input past work history into a generation AI and have the generation AI select the optimal consolidation method.

[0036] The creation unit can design an efficient structure by taking into account the interrelationships between the business procedures of each department when creating the knowledge base. For example, the creation unit can design an efficient structure by taking into account the interrelationships between the business procedures of each department when creating the knowledge base. For example, the creation unit can analyze the dependencies between the business procedures of each department and organize and structure the interrelated procedures. The creation unit can also visually display the interrelationships between the business procedures and design an easy-to-understand structure. The creation unit can also design a structure that provides efficient navigation by taking into account the interrelationships between the business procedures. This enables the creation of an efficient knowledge base by designing an optimal structure by taking into account the interrelationships between the business procedures of each department. Some or all of the above-described processing in the creation unit can be performed using, or without, AI. For example, the creation unit can input the interrelationships between the business procedures into a generation AI and have the generation AI design an efficient structure.

[0037] The creation department can select an efficient consolidation method by taking geographical factors into consideration when consolidating the manuals and handover documents of each department. For example, the creation department can select an efficient consolidation method by taking geographical factors into consideration when consolidating the manuals and handover documents of each department. For example, the creation department can prioritize consolidating manuals from geographically close departments to provide efficient access. The creation department can also select a consolidation method suitable for remote work by taking geographical factors into consideration. The creation department can also optimize the sharing of business procedures based on geographical factors. This enables the creation of an efficient knowledge base by selecting the optimal consolidation method by taking geographical factors into consideration. Some or all of the above-described processing in the creation department can be performed using, or without, AI. For example, the creation department can input geographical factors into a generation AI and have the generation AI select the optimal consolidation method.

[0038] The creation unit can optimize the content of the knowledge base by referring to best practices from other municipalities when creating it. For example, the creation unit can refer to best practices from other municipalities and incorporate them into the knowledge base. The creation unit can also improve business procedures based on the best practices of other municipalities. The creation unit can also enrich the content of the knowledge base by sharing information with other municipalities. This allows for efficient creation of the knowledge base by enriching the content with reference to best practices from other municipalities. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input best practices from other municipalities into a generation AI and have the generation AI optimize the content.

[0039] The creation unit can dynamically update the content of the knowledge base when creating it, taking into account the progress of the work and the frequency of use. For example, the creation unit dynamically updates the content of the knowledge base when creating it, taking into account the progress of the work and the frequency of use. For example, the creation unit monitors the progress of the work in real time and updates the content of the knowledge base as needed. The creation unit can also prioritize updating frequently used procedures to provide the latest information. The creation unit can also dynamically optimize the content of the knowledge base based on the progress of the work and the frequency of use. This enables efficient creation of a knowledge base by dynamically updating the content taking into account the progress of the work and the frequency of use. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to dynamically update the content.

[0040] The efficiency improvement unit can analyze usage logs of internal systems and tools in detail to improve the accuracy of efficiency improvements. For example, the efficiency improvement unit can analyze usage logs of internal systems and tools in detail to improve the accuracy of efficiency improvements. For example, the efficiency improvement unit prioritizes optimization of frequently used functions based on usage logs. The efficiency improvement unit can also identify areas where errors frequently occur from the usage logs and implement improvements. The efficiency improvement unit can also analyze usage logs to understand and optimize user behavior patterns. Thus, detailed analysis of usage logs of internal systems and tools improves the accuracy of optimization. Some or all of the above-described processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input usage logs into a generation AI and have the generation AI execute efficiency improvement operations.

[0041] When monitoring a business process, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures as necessary. For example, when monitoring a business process, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures as necessary. For example, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures if a delay occurs. The efficiency improvement unit can also propose efficient procedures based on the progress of the work. The efficiency improvement unit can also monitor the progress of the work and dynamically adjust the procedures as necessary. This enables efficient work by monitoring the progress of the work in real time and reevaluating the procedures as necessary. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input work progress data to a generation AI and cause the generation AI to reevaluate the procedures.

[0042] The efficiency improvement unit can design efficient procedures by taking into account the interrelationships between the business procedures of each department when improving the efficiency of the procedures. For example, the efficiency improvement unit can analyze the dependencies between the business procedures of each department and optimize the interrelated procedures. The efficiency improvement unit can also visually display the interrelationships between the business procedures and design procedures that are easy to understand. The efficiency improvement unit can also design efficient procedures by taking into account the interrelationships between the business procedures. This enables efficient work by designing optimal procedures by taking into account the interrelationships between the business procedures of each department. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input the interrelationships between business procedures into a generation AI and have the generation AI design an efficient procedure.

[0043] The efficiency improvement unit can take geographical factors into consideration when analyzing usage logs of internal systems and tools to improve efficiency. For example, the efficiency improvement unit can take geographical factors into consideration when analyzing usage logs of internal systems and tools to improve efficiency. For example, the efficiency improvement unit can prioritize analysis of usage logs from geographically close departments to perform efficient optimization. The efficiency improvement unit can also take geographical factors into consideration to perform optimization suitable for remote work. The efficiency improvement unit can also optimize the sharing of work procedures based on geographical factors. This enables efficient work by optimizing taking geographical factors into consideration. Some or all of the above-mentioned processing in the efficiency improvement unit can be performed using, for example, AI, or can be performed without using AI. For example, the efficiency improvement unit can input geographical factors into a generation AI and have the generation AI perform efficiency improvements.

[0044] The efficiency improvement unit can improve the accuracy of efficiency by referring to best practices from other municipalities when monitoring business processes. For example, the efficiency improvement unit can improve the accuracy of efficiency by referring to best practices from other municipalities when monitoring business processes. For example, the efficiency improvement unit can refer to successful cases from other municipalities and incorporate them into optimization. The efficiency improvement unit can also improve business procedures based on best practices from other municipalities. The efficiency improvement unit can also improve the accuracy of optimization by sharing information with other municipalities. By doing so, improving the accuracy of optimization by referring to best practices from other municipalities enables efficient work. Some or all of the above-described processing in the efficiency improvement unit can be performed, for example, using AI, or can be performed without AI. For example, the efficiency improvement unit can input best practices from other municipalities into a generation AI and have the generation AI execute improvements to the accuracy of efficiency.

[0045] The efficiency improvement unit can dynamically improve the efficiency of a procedure by taking into account the progress of the work and the frequency of use. For example, the efficiency improvement unit can dynamically improve the efficiency of a procedure by taking into account the progress of the work and the frequency of use. For example, the efficiency improvement unit monitors the progress of the work in real time and dynamically optimizes the procedure as needed. The efficiency improvement unit can also prioritize optimization of frequently used procedures and provide the latest information. The efficiency improvement unit can dynamically optimize the procedure based on the progress of the work and the frequency of use. This enables efficient work by dynamically optimizing the procedure by taking into account the progress of the work and the frequency of use. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to perform dynamic efficiency improvement.

[0046] The generation unit can improve the efficiency of the generation algorithm by referring to past learning data when automatically generating a manual tailored to a skill level. For example, the generation unit can optimize the generation algorithm by referring to past learning data when automatically generating a manual tailored to a skill level. For example, the generation unit generates a manual for beginners based on past learning data. The generation unit can also generate an advanced manual for experts from past learning data. The generation unit can also analyze past learning data and select an optimal generation algorithm. This enables efficient work by optimizing the generation algorithm by referring to past learning data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past learning data into the generation AI and cause the generation AI to optimize the generation algorithm.

[0047] The generation unit can optimize the content by reflecting the user's past feedback when creating training content using AI voice. For example, the generation unit improves the content by reflecting the user's past feedback when creating training content using AI voice. For example, the generation unit improves the content of the training content based on the user's past feedback. The generation unit can also create content by emphasizing particularly highly rated parts based on the user's feedback. The generation unit can also analyze the user's feedback and provide training content that reflects areas for improvement. This enables efficient work by improving the content by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to optimize the training content.

[0048] The generation unit can design efficient content by taking into account the interrelationships between the business procedures of each department when automatically generating a manual. For example, the generation unit can design efficient content by taking into account the interrelationships between the business procedures of each department when automatically generating a manual. For example, the generation unit can analyze the dependencies between the business procedures of each department and generate a manual by consolidating the interrelated procedures. The generation unit can also visually display the interrelationships between the business procedures to design an easy-to-understand manual. The generation unit can also design an efficient manual by taking into account the interrelationships between the business procedures. This enables efficient work by designing optimal content by taking into account the interrelationships between the business procedures of each department. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the interrelationships between the business procedures into a generation AI and have the generation AI design efficient content.

[0049] The generation unit can optimize the content by taking geographical factors into consideration when automatically generating a manual tailored to a skill level. For example, the generation unit customizes the content by taking geographical factors into consideration when automatically generating a manual tailored to a skill level. For example, the generation unit generates a manual by prioritizing customization of business procedures for geographically close departments. The generation unit can also generate a manual suitable for remote work by taking geographical factors into consideration. The generation unit can also generate a manual that optimizes the sharing of business procedures based on geographical factors. This enables efficient work by customizing the content by taking geographical factors into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical factors into the generation AI and cause the generation AI to optimize the content.

[0050] When creating training content using AI voice, the generation unit can optimize the content by referring to best practices from other local governments. For example, when creating training content using AI voice, the generation unit can enhance the content by referring to best practices from other local governments. For example, the generation unit can refer to success stories from other local governments and incorporate them into the training content. The generation unit can also improve the training content based on best practices from other local governments. The generation unit can also enhance the content of the training content by sharing information with other local governments. This enables efficient work by enhancing the content by referring to best practices from other local governments. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input best practices from other local governments into the generation AI and have the generation AI optimize the content.

[0051] The generation unit can dynamically update the content of the manual when automatically generating it, taking into account the progress of the work and the frequency of use. For example, the generation unit can dynamically update the content of the manual when automatically generating it, taking into account the progress of the work and the frequency of use. For example, the generation unit can monitor the progress of the work in real time and update the content of the manual as needed. The generation unit can also prioritize updating frequently used procedures to provide the latest information. The generation unit can also dynamically optimize the content of the manual based on the progress of the work and the frequency of use. This enables efficient work by dynamically updating the content taking into account the progress of the work and the frequency of use. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to dynamically update the content.

[0052] When directly responding to an inquiry from a resident, the providing unit can select an efficient response method by referring to past inquiry history. For example, when directly responding to an inquiry from a resident, the providing unit selects an optimal response method by referring to past inquiry history. For example, the providing unit provides optimal answers to frequently asked questions based on the past inquiry history. The providing unit can also suggest solutions to specific problems from the past inquiry history. The providing unit can also analyze the past inquiry history and select an optimal response method. This enables efficient work by selecting an optimal response method by referring to the past inquiry history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past inquiry history into a generation AI and have the generation AI select a response method.

[0053] The providing unit can optimize the response content by taking into account the resident's attribute information when providing the virtual counter. For example, the providing unit customizes the response content by taking into account the resident's attribute information when providing the virtual counter. For example, the providing unit provides an appropriate response method depending on the resident's age and gender. The providing unit can also provide an individually customized response based on the resident's past usage history. The providing unit can also provide the optimal response content by taking into account the resident's attribute information. This enables efficient work by customizing the response content by taking into account the resident's attribute information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the resident's attribute information into a generating AI and cause the generating AI to optimize the response content.

[0054] When providing a virtual help desk, the provision unit can design an efficient response by taking into account the interrelationships between the business procedures of each department. For example, when providing a virtual help desk, the provision unit designs an efficient response by taking into account the interrelationships between the business procedures of each department. For example, the provision unit analyzes the dependencies between the business procedures of each department and optimizes the interrelated procedures to respond. The provision unit can also visually display the interrelationships between the business procedures to design an easy-to-understand response. The provision unit can also design an efficient response by taking into account the interrelationships between the business procedures. This enables efficient work by designing an optimal response by taking into account the interrelationships between the business procedures of each department. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the interrelationships between business procedures into a generation AI and have the generation AI execute the design of an efficient response.

[0055] The providing unit can select an efficient response method by taking geographical factors into consideration when directly responding to inquiries from residents. For example, when directly responding to inquiries from residents, the providing unit selects the optimal response method by taking geographical factors into consideration. For example, the providing unit provides a quick response to inquiries from residents who are geographically close. The providing unit can also select a method suitable for remote response by taking geographical factors into consideration. The providing unit can also select the optimal response method based on geographical factors. This enables efficient work by selecting the optimal response method by taking geographical factors into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical factors into a generating AI and cause the generating AI to select a response method.

[0056] The provision unit can optimize the response content by referring to best practices from other local governments when providing the virtual counter. For example, when providing the virtual counter, the provision unit enhances the response content by referring to best practices from other local governments. For example, the provision unit references successful cases from other local governments and reflects them in the response content. The provision unit can also improve the response method based on the best practices of other local governments. The provision unit can also enhance the response content by sharing information with other local governments. This enables efficient work by enhancing the response content by referring to best practices from other local governments. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input best practices from other local governments into a generation AI and have the generation AI optimize the response content.

[0057] The providing unit can dynamically update the response content taking into account the progress of the business and the frequency of use when providing the virtual help desk. For example, the providing unit dynamically updates the response content taking into account the progress of the business and the frequency of use when providing the virtual help desk. For example, the providing unit monitors the progress of the business in real time and updates the response content as necessary. The providing unit can also prioritize updating the response content for inquiries with high frequency of use. The providing unit can also dynamically optimize the response content based on the progress of the business and the frequency of use. This enables efficient work by dynamically updating the response content taking into account the progress of the business and the frequency of use. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the progress of the business and the frequency of use into the generating AI and cause the generating AI to dynamically update the response content.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The knowledge base platform may further include a feedback collection unit. The feedback collection unit may collect feedback from users and use it to improve the knowledge base. For example, the feedback collection unit may collect users' impressions and suggestions for improvement after using the manual and update the content of the knowledge base. The feedback collection unit may also evaluate the user's satisfaction when using the virtual help desk and improve the response method. Furthermore, the feedback collection unit may analyze user feedback and optimize the structure and content of the knowledge base. This makes it possible to continuously improve the knowledge base by reflecting user feedback.

[0060] The Efficiency Improvement Department can improve the accuracy of efficiency improvements by conducting detailed analysis of usage logs of internal systems and tools. For example, the Efficiency Improvement Department can prioritize optimization of frequently used functions based on usage logs. The Efficiency Improvement Department can also identify areas where errors frequently occur from usage logs and implement improvements. The Efficiency Improvement Department can also analyze usage logs to understand and optimize user behavior patterns. In this way, detailed analysis of usage logs of internal systems and tools improves the accuracy of optimization.

[0061] When directly responding to inquiries from residents, the information providing unit can refer to past inquiry history to select an efficient response method. For example, the information providing unit can provide optimal answers to frequently asked questions based on the past inquiry history. The information providing unit can also suggest solutions to specific problems from the past inquiry history. The information providing unit can also analyze the past inquiry history and select the optimal response method. This allows for efficient work by selecting the optimal response method by referring to the past inquiry history.

[0062] When creating the knowledge base, the creation department can optimize the content by referring to best practices from other local governments. For example, the creation department can refer to successful cases from other local governments and reflect them in the knowledge base. The creation department can also improve business procedures based on the best practices of other local governments. The creation department can also enrich the content of the knowledge base by sharing information with other local governments. This makes it possible to create an efficient knowledge base by enriching the content by referring to best practices from other local governments.

[0063] When automatically generating manuals tailored to skill levels, the generation unit can refer to past learning data to improve the efficiency of the generation algorithm. For example, the generation unit generates a manual for beginners based on past learning data. The generation unit can also generate advanced manuals for experts from past learning data. The generation unit can also analyze past learning data and select the optimal generation algorithm. This allows for efficient work by optimizing the generation algorithm by referring to past learning data.

[0064] When providing the virtual help desk, the provision unit can optimize the response content by taking into account the resident's attribute information. For example, the provision unit can provide an appropriate response method depending on the resident's age and gender. The provision unit can also provide an individually customized response based on the resident's past usage history. The provision unit can also provide the optimal response content by taking into account the resident's attribute information. This allows for efficient work by customizing the response content by taking into account the resident's attribute information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The creation department creates a knowledge base. For example, the creation department consolidates the manuals and handover documents from each department and creates a unified knowledge base. Step 2: The Efficiency Division streamlines procedures based on the knowledge base created by the Creation Division. For example, the Efficiency Division monitors usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. Step 3: The generation unit automatically generates manuals based on the procedures streamlined by the efficiency unit. For example, the generation unit automatically generates manuals tailored to skill levels and creates training content using AI voice. Step 4: The providing unit provides a virtual counter based on the manual generated by the generating unit. For example, the providing unit provides a virtual counter that directly responds to inquiries from residents.

[0067] (Example 2) A knowledge base platform according to an embodiment of the present invention is a system that prevents local government operations from becoming personalized and promotes the creation of manuals for operational procedures and know-how. This system utilizes generative AI to create a knowledge base, optimize procedures, automatically generate manuals, and provide a virtual contact point. For example, the knowledge base platform consolidates manuals and handover documents from each department to create a unified knowledge base. Next, it monitors usage logs and business processes for internal systems and tools, automatically updating the knowledge base and optimizing procedures. Furthermore, it automatically generates manuals tailored to skill levels and creates training content using AI voice, providing up-to-date and appropriate human resource development content. Finally, it utilizes the knowledge base to provide a virtual contact point that directly responds to inquiries from residents. This enables the knowledge base platform to centrally manage information and provide optimal procedures, preventing the personalized use of know-how. Furthermore, using the knowledge base as a business support system or virtual contact point improves the quality of operations and reduces human resource resources through increased efficiency. This knowledge base platform can alleviate the human resource shortages faced by local governments and improve the convenience and satisfaction of citizen services.

[0068] A knowledge base platform according to an embodiment includes a creation unit, an efficiency improvement unit, a generation unit, and a provision unit. The creation unit creates a knowledge base. For example, the creation unit consolidates manuals and handover documents from each department to create a unified knowledge base. The efficiency improvement unit streamlines procedures based on the knowledge base created by the creation unit. For example, the efficiency improvement unit monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit automatically generates manuals based on the procedures streamlined by the efficiency improvement unit. For example, the generation unit automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit provides a virtual counter based on the manuals generated by the generation unit. For example, the provision unit provides a virtual counter that directly responds to inquiries from residents. This enables the knowledge base platform to create a knowledge base, streamline procedures, automatically generate manuals, and provide a virtual counter. As a result, the knowledge base platform according to an embodiment can alleviate the human resource shortage issues faced by local governments and improve the convenience and satisfaction of citizen services.

[0069] The creation unit can consolidate the manuals and handover documents of each department and create an integrated knowledge base. For example, the creation unit consolidates the manuals and handover documents of each department and creates a unified knowledge base. For example, the creation unit analyzes the business procedures and know-how of each department in detail and compiles them into a common format. By consolidating the manuals and handover documents of each department into a unified knowledge base, centralized management of information becomes possible. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the manuals and handover documents of each department into a generation AI and have the generation AI create a unified knowledge base.

[0070] The efficiency improvement department can monitor usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. For example, the efficiency improvement department can monitor usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. For example, the efficiency improvement department can monitor the progress and frequency of use of work in real time and review procedures as necessary. This makes it possible to automatically update the knowledge base and optimize procedures by monitoring usage logs and business processes of internal systems and tools. Some or all of the above-mentioned processing in the efficiency improvement department can be performed using, for example, AI, or without AI. For example, the efficiency improvement department can input usage logs of internal systems and tools into a generation AI and have the generation AI automatically update the knowledge base and optimize procedures.

[0071] The generation unit can automatically generate manuals tailored to skill levels and create training content using AI voice. The generation unit, for example, automatically generates manuals tailored to skill levels. For example, the generation unit automatically generates content tailored to skill levels, from basic manuals for new employees to advanced manuals for experienced employees. The generation unit also creates training content using AI voice. For example, the generation unit uses AI voice to create training content for employees. This makes it possible to automatically generate manuals tailored to skill levels and create training content using AI voice. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can cause a generation AI to automatically generate a manual tailored to skill levels.

[0072] The providing unit can provide a virtual counter that directly responds to inquiries from residents. The providing unit, for example, provides a virtual counter that directly responds to inquiries from residents. For example, when a resident comes to the counter, the providing unit has an AI automatically respond and provide the necessary information. This provides a virtual counter that directly responds to inquiries from residents, thereby reducing the burden on staff. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may have a generating AI respond to inquiries from residents.

[0073] The creation unit can estimate the user's emotions and adjust the timing of creating the knowledge base based on the estimated user emotions. The creation unit, for example, estimates the user's emotions and adjusts the timing of creating the knowledge base based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit delays the creation of the knowledge base, allowing the user to work in a relaxed state. Furthermore, if the user is concentrating, the creation unit can immediately start creating the knowledge base, allowing the user to work efficiently. Furthermore, if the user is tired, the creation unit can temporarily suspend the creation of the knowledge base and resume it after a break. This allows the user to work efficiently by adjusting the timing of creating the knowledge base based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the creation unit can input user emotion data into the generation AI and have the generation AI adjust the timing of creating the knowledge base.

[0074] The creation unit can set priorities based on the importance of the work when analyzing the work procedures of each department in detail and compiling them into a common format. For example, the creation unit can set priorities based on the importance of the work when analyzing the work procedures of each department in detail and compiling them into a common format. For example, the creation unit can prioritize important work procedures and quickly add them to the knowledge base. The creation unit can also postpone less important work procedures, allowing for efficient resource allocation. The creation unit can also periodically review the importance of the work procedures and dynamically adjust the priorities. This enables efficient creation of a knowledge base by setting priorities based on the importance of the work. Some or all of the above-described processing in the creation unit can be performed using, for example, AI, or without AI. For example, the creation unit can input the importance of the work procedures into a generation AI and have the generation AI set the priorities.

[0075] The creation unit can select an efficient consolidation method by referring to past work history when consolidating manuals and handover documents for each department. For example, when consolidating manuals and handover documents for each department, the creation unit can select an efficient consolidation method by referring to past work history. For example, the creation unit analyzes past work history and prioritizes consolidating frequently used procedures. The creation unit can also identify procedures that frequently cause errors from the past work history and consolidate them by incorporating improvements. The creation unit can also optimize and consolidate work flows based on the past work history. This enables the creation of an efficient knowledge base by selecting the optimal consolidation method by referring to the past work history. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input past work history into a generation AI and have the generation AI select the optimal consolidation method.

[0076] The creation unit can design an efficient structure by taking into account the interrelationships between the business procedures of each department when creating the knowledge base. For example, the creation unit can design an efficient structure by taking into account the interrelationships between the business procedures of each department when creating the knowledge base. For example, the creation unit can analyze the dependencies between the business procedures of each department and organize and structure the interrelated procedures. The creation unit can also visually display the interrelationships between the business procedures and design an easy-to-understand structure. The creation unit can also design a structure that provides efficient navigation by taking into account the interrelationships between the business procedures. This enables the creation of an efficient knowledge base by designing an optimal structure by taking into account the interrelationships between the business procedures of each department. Some or all of the above-described processing in the creation unit can be performed using, or without, AI. For example, the creation unit can input the interrelationships between the business procedures into a generation AI and have the generation AI design an efficient structure.

[0077] The creation unit can estimate a user's emotion and adjust the content of the knowledge base based on the estimated user emotion. For example, the creation unit can estimate a user's emotion and adjust the content of the knowledge base based on the estimated user emotion. For example, if the user is stressed, the creation unit can provide concise and to-the-point content. Furthermore, if the user is relaxed, the creation unit can provide content with detailed explanations. Furthermore, if the user is excited, the creation unit can provide visually appealing content. This enables efficient work by customizing the content of the knowledge base based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the creation unit can be performed using, for example, an AI, or without an AI. For example, the creation unit can input user emotion data into the generation AI and cause the generation AI to adjust the content of the knowledge base.

[0078] The creation department can select an efficient consolidation method by taking geographical factors into consideration when consolidating the manuals and handover documents of each department. For example, the creation department can select an efficient consolidation method by taking geographical factors into consideration when consolidating the manuals and handover documents of each department. For example, the creation department can prioritize consolidating manuals from geographically close departments to provide efficient access. The creation department can also select a consolidation method suitable for remote work by taking geographical factors into consideration. The creation department can also optimize the sharing of business procedures based on geographical factors. This enables the creation of an efficient knowledge base by selecting the optimal consolidation method by taking geographical factors into consideration. Some or all of the above-described processing in the creation department can be performed using, or without, AI. For example, the creation department can input geographical factors into a generation AI and have the generation AI select the optimal consolidation method.

[0079] The creation unit can optimize the content of the knowledge base by referring to best practices from other municipalities when creating it. For example, the creation unit can refer to best practices from other municipalities and incorporate them into the knowledge base. The creation unit can also improve business procedures based on the best practices of other municipalities. The creation unit can also enrich the content of the knowledge base by sharing information with other municipalities. This allows for efficient creation of the knowledge base by enriching the content with reference to best practices from other municipalities. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without AI. For example, the creation unit can input best practices from other municipalities into a generation AI and have the generation AI optimize the content.

[0080] The creation unit can dynamically update the content of the knowledge base when creating it, taking into account the progress of the work and the frequency of use. For example, the creation unit dynamically updates the content of the knowledge base when creating it, taking into account the progress of the work and the frequency of use. For example, the creation unit monitors the progress of the work in real time and updates the content of the knowledge base as needed. The creation unit can also prioritize updating frequently used procedures to provide the latest information. The creation unit can also dynamically optimize the content of the knowledge base based on the progress of the work and the frequency of use. This enables efficient creation of a knowledge base by dynamically updating the content taking into account the progress of the work and the frequency of use. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to dynamically update the content.

[0081] The efficiency improvement unit can estimate the user's emotions and adjust the method for improving the efficiency of a procedure based on the estimated user emotions. For example, the efficiency improvement unit can estimate the user's emotions and adjust the method for improving the efficiency of a procedure based on the estimated user emotions. For example, if the user is feeling stressed, the efficiency improvement unit can simplify the procedure to reduce the burden. Furthermore, if the user is relaxed, the efficiency improvement unit can provide detailed instructions to deepen understanding. Furthermore, if the user is in a hurry, the efficiency improvement unit can provide instructions that can be completed quickly. This enables efficient work by adjusting the method for optimizing the procedure based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the efficiency improvement unit can be performed using, for example, an AI. For example, the efficiency improvement unit can input user emotion data into the generation AI and cause the generation AI to adjust the method for improving the efficiency of the procedure.

[0082] The efficiency improvement unit can analyze usage logs of internal systems and tools in detail to improve the accuracy of efficiency improvements. For example, the efficiency improvement unit can analyze usage logs of internal systems and tools in detail to improve the accuracy of efficiency improvements. For example, the efficiency improvement unit prioritizes optimization of frequently used functions based on usage logs. The efficiency improvement unit can also identify areas where errors frequently occur from the usage logs and implement improvements. The efficiency improvement unit can also analyze usage logs to understand and optimize user behavior patterns. Thus, detailed analysis of usage logs of internal systems and tools improves the accuracy of optimization. Some or all of the above-described processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input usage logs into a generation AI and have the generation AI execute efficiency improvement operations.

[0083] When monitoring a business process, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures as necessary. For example, when monitoring a business process, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures as necessary. For example, the efficiency improvement unit can monitor the progress of the work in real time and reevaluate the procedures if a delay occurs. The efficiency improvement unit can also propose efficient procedures based on the progress of the work. The efficiency improvement unit can also monitor the progress of the work and dynamically adjust the procedures as necessary. This enables efficient work by monitoring the progress of the work in real time and reevaluating the procedures as necessary. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input work progress data to a generation AI and cause the generation AI to reevaluate the procedures.

[0084] The efficiency improvement unit can design efficient procedures by taking into account the interrelationships between the business procedures of each department when improving the efficiency of the procedures. For example, the efficiency improvement unit can analyze the dependencies between the business procedures of each department and optimize the interrelated procedures. The efficiency improvement unit can also visually display the interrelationships between the business procedures and design procedures that are easy to understand. The efficiency improvement unit can also design efficient procedures by taking into account the interrelationships between the business procedures. This enables efficient work by designing optimal procedures by taking into account the interrelationships between the business procedures of each department. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input the interrelationships between business procedures into a generation AI and have the generation AI design an efficient procedure.

[0085] The efficiency improvement unit can estimate the user's emotions and adjust the display method of the streamlined procedure based on the estimated user emotions. For example, the efficiency improvement unit can estimate the user's emotions and adjust the display method of the streamlined procedure based on the estimated user emotions. For example, if the user is stressed, the efficiency improvement unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the efficiency improvement unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the efficiency improvement unit can provide a display method that focuses on the main points. This enables efficient work by adjusting the display method of the optimized procedure based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the efficiency improvement unit can be performed using, for example, an AI. For example, the efficiency improvement unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0086] The efficiency improvement unit can take geographical factors into consideration when analyzing usage logs of internal systems and tools to improve efficiency. For example, the efficiency improvement unit can take geographical factors into consideration when analyzing usage logs of internal systems and tools to improve efficiency. For example, the efficiency improvement unit can prioritize analysis of usage logs from geographically close departments to perform efficient optimization. The efficiency improvement unit can also take geographical factors into consideration to perform optimization suitable for remote work. The efficiency improvement unit can also optimize the sharing of work procedures based on geographical factors. This enables efficient work by optimizing taking geographical factors into consideration. Some or all of the above-mentioned processing in the efficiency improvement unit can be performed using, for example, AI, or can be performed without using AI. For example, the efficiency improvement unit can input geographical factors into a generation AI and have the generation AI perform efficiency improvements.

[0087] The efficiency improvement unit can improve the accuracy of efficiency by referring to best practices from other municipalities when monitoring business processes. For example, the efficiency improvement unit can improve the accuracy of efficiency by referring to best practices from other municipalities when monitoring business processes. For example, the efficiency improvement unit can refer to successful cases from other municipalities and incorporate them into optimization. The efficiency improvement unit can also improve business procedures based on best practices from other municipalities. The efficiency improvement unit can also improve the accuracy of optimization by sharing information with other municipalities. By doing so, improving the accuracy of optimization by referring to best practices from other municipalities enables efficient work. Some or all of the above-described processing in the efficiency improvement unit can be performed, for example, using AI, or can be performed without AI. For example, the efficiency improvement unit can input best practices from other municipalities into a generation AI and have the generation AI execute improvements to the accuracy of efficiency.

[0088] The efficiency improvement unit can dynamically improve the efficiency of a procedure by taking into account the progress of the work and the frequency of use. For example, the efficiency improvement unit can dynamically improve the efficiency of a procedure by taking into account the progress of the work and the frequency of use. For example, the efficiency improvement unit monitors the progress of the work in real time and dynamically optimizes the procedure as needed. The efficiency improvement unit can also prioritize optimization of frequently used procedures and provide the latest information. The efficiency improvement unit can dynamically optimize the procedure based on the progress of the work and the frequency of use. This enables efficient work by dynamically optimizing the procedure by taking into account the progress of the work and the frequency of use. Some or all of the above-mentioned processing in the efficiency improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the efficiency improvement unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to perform dynamic efficiency improvement.

[0089] The generation unit can estimate the user's emotions and optimize the manual generation method based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the manual generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a concise manual that focuses on the main points. Furthermore, if the user is relaxed, the generation unit can generate a manual that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a manual that can be completed quickly. This enables efficient work by adjusting the manual generation method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the manual generation method.

[0090] The generation unit can improve the efficiency of the generation algorithm by referring to past learning data when automatically generating a manual tailored to a skill level. For example, the generation unit can optimize the generation algorithm by referring to past learning data when automatically generating a manual tailored to a skill level. For example, the generation unit generates a manual for beginners based on past learning data. The generation unit can also generate an advanced manual for experts from past learning data. The generation unit can also analyze past learning data and select an optimal generation algorithm. This enables efficient work by optimizing the generation algorithm by referring to past learning data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past learning data into the generation AI and cause the generation AI to optimize the generation algorithm.

[0091] The generation unit can optimize the content by reflecting the user's past feedback when creating training content using AI voice. For example, the generation unit improves the content by reflecting the user's past feedback when creating training content using AI voice. For example, the generation unit improves the content of the training content based on the user's past feedback. The generation unit can also create content by emphasizing particularly highly rated parts based on the user's feedback. The generation unit can also analyze the user's feedback and provide training content that reflects areas for improvement. This enables efficient work by improving the content by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to optimize the training content.

[0092] The generation unit can design efficient content by taking into account the interrelationships between the business procedures of each department when automatically generating a manual. For example, the generation unit can design efficient content by taking into account the interrelationships between the business procedures of each department when automatically generating a manual. For example, the generation unit can analyze the dependencies between the business procedures of each department and generate a manual by consolidating the interrelated procedures. The generation unit can also visually display the interrelationships between the business procedures to design an easy-to-understand manual. The generation unit can also design an efficient manual by taking into account the interrelationships between the business procedures. This enables efficient work by designing optimal content by taking into account the interrelationships between the business procedures of each department. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the interrelationships between the business procedures into a generation AI and have the generation AI design efficient content.

[0093] The generation unit can estimate the user's emotions and optimize the display method of the generated manual based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the display method of the generated manual based on the estimated user emotions. For example, if the user is stressed, the generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. This enables efficient work by adjusting the display method of the generated manual based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0094] The generation unit can optimize the content by taking geographical factors into consideration when automatically generating a manual tailored to a skill level. For example, the generation unit customizes the content by taking geographical factors into consideration when automatically generating a manual tailored to a skill level. For example, the generation unit generates a manual by prioritizing customization of business procedures for geographically close departments. The generation unit can also generate a manual suitable for remote work by taking geographical factors into consideration. The generation unit can also generate a manual that optimizes the sharing of business procedures based on geographical factors. This enables efficient work by customizing the content by taking geographical factors into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical factors into the generation AI and cause the generation AI to optimize the content.

[0095] When creating training content using AI voice, the generation unit can optimize the content by referring to best practices from other local governments. For example, when creating training content using AI voice, the generation unit can enhance the content by referring to best practices from other local governments. For example, the generation unit can refer to success stories from other local governments and incorporate them into the training content. The generation unit can also improve the training content based on best practices from other local governments. The generation unit can also enhance the content of the training content by sharing information with other local governments. This enables efficient work by enhancing the content by referring to best practices from other local governments. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input best practices from other local governments into the generation AI and have the generation AI optimize the content.

[0096] The generation unit can dynamically update the content of the manual when automatically generating it, taking into account the progress of the work and the frequency of use. For example, the generation unit can dynamically update the content of the manual when automatically generating it, taking into account the progress of the work and the frequency of use. For example, the generation unit can monitor the progress of the work in real time and update the content of the manual as needed. The generation unit can also prioritize updating frequently used procedures to provide the latest information. The generation unit can also dynamically optimize the content of the manual based on the progress of the work and the frequency of use. This enables efficient work by dynamically updating the content taking into account the progress of the work and the frequency of use. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the progress of the work and the frequency of use into the generation AI and cause the generation AI to dynamically update the content.

[0097] The providing unit can estimate the user's emotions and optimize the virtual counter's response method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the virtual counter's response method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can respond in a gentle tone, providing a sense of security. Furthermore, if the user is relaxed, the providing unit can respond in a friendly manner, providing a sense of familiarity. Furthermore, if the user is in a hurry, the providing unit can respond quickly and concisely. This enables efficient work by adjusting the virtual counter's response method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the response method.

[0098] When directly responding to an inquiry from a resident, the providing unit can select an efficient response method by referring to past inquiry history. For example, when directly responding to an inquiry from a resident, the providing unit selects an optimal response method by referring to past inquiry history. For example, the providing unit provides optimal answers to frequently asked questions based on the past inquiry history. The providing unit can also suggest solutions to specific problems from the past inquiry history. The providing unit can also analyze the past inquiry history and select an optimal response method. This enables efficient work by selecting an optimal response method by referring to the past inquiry history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past inquiry history into a generation AI and have the generation AI select a response method.

[0099] The providing unit can optimize the response content by taking into account the resident's attribute information when providing the virtual counter. For example, the providing unit customizes the response content by taking into account the resident's attribute information when providing the virtual counter. For example, the providing unit provides an appropriate response method depending on the resident's age and gender. The providing unit can also provide an individually customized response based on the resident's past usage history. The providing unit can also provide the optimal response content by taking into account the resident's attribute information. This enables efficient work by customizing the response content by taking into account the resident's attribute information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the resident's attribute information into a generating AI and cause the generating AI to optimize the response content.

[0100] When providing a virtual help desk, the provision unit can design an efficient response by taking into account the interrelationships between the business procedures of each department. For example, when providing a virtual help desk, the provision unit designs an efficient response by taking into account the interrelationships between the business procedures of each department. For example, the provision unit analyzes the dependencies between the business procedures of each department and optimizes the interrelated procedures to respond. The provision unit can also visually display the interrelationships between the business procedures to design an easy-to-understand response. The provision unit can also design an efficient response by taking into account the interrelationships between the business procedures. This enables efficient work by designing an optimal response by taking into account the interrelationships between the business procedures of each department. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the interrelationships between business procedures into a generation AI and have the generation AI execute the design of an efficient response.

[0101] The providing unit can estimate the user's emotions and optimize the display method of the virtual counter based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the virtual counter based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This enables efficient work by adjusting the display method of the virtual counter based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0102] The providing unit can select an efficient response method by taking geographical factors into consideration when directly responding to inquiries from residents. For example, when directly responding to inquiries from residents, the providing unit selects the optimal response method by taking geographical factors into consideration. For example, the providing unit provides a quick response to inquiries from residents who are geographically close. The providing unit can also select a method suitable for remote response by taking geographical factors into consideration. The providing unit can also select the optimal response method based on geographical factors. This enables efficient work by selecting the optimal response method by taking geographical factors into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical factors into a generating AI and cause the generating AI to select a response method.

[0103] The provision unit can optimize the response content by referring to best practices from other local governments when providing the virtual counter. For example, when providing the virtual counter, the provision unit enhances the response content by referring to best practices from other local governments. For example, the provision unit references successful cases from other local governments and reflects them in the response content. The provision unit can also improve the response method based on the best practices of other local governments. The provision unit can also enhance the response content by sharing information with other local governments. This enables efficient work by enhancing the response content by referring to best practices from other local governments. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input best practices from other local governments into a generation AI and have the generation AI optimize the response content.

[0104] The providing unit can dynamically update the response content taking into account the progress of the business and the frequency of use when providing the virtual help desk. For example, the providing unit dynamically updates the response content taking into account the progress of the business and the frequency of use when providing the virtual help desk. For example, the providing unit monitors the progress of the business in real time and updates the response content as necessary. The providing unit can also prioritize updating the response content for inquiries with high frequency of use. The providing unit can also dynamically optimize the response content based on the progress of the business and the frequency of use. This enables efficient work by dynamically updating the response content taking into account the progress of the business and the frequency of use. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the progress of the business and the frequency of use into the generating AI and cause the generating AI to dynamically update the response content. === Hard Collateral 1-1 === Each of the multiple elements, including the creation unit, efficiency improvement unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the creation unit consolidates manuals and handover documents from each department using the control unit 46A of the smart device 14 to create a unified knowledge base. The efficiency improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides a virtual help desk that directly responds to inquiries from residents. === Hard Collateral 1-2 === Each of the multiple elements, including the creation unit, efficiency improvement unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the creation unit consolidates manuals and handover documents from each department using the control unit 46A of the smart glasses 214 to create a unified knowledge base. The efficiency improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides a virtual help desk that directly responds to inquiries from residents. === Hard Collateral 1-3 === Each of the multiple elements, including the creation unit, efficiency improvement unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the creation unit consolidates manuals and handover documents from each department using the control unit 46A of the headset terminal 314 to create a unified knowledge base. The efficiency improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit, realized, for example, by the control unit 46A of the headset terminal 314, provides a virtual help desk that directly responds to inquiries from residents. === Hard Collateral 1-4 === Each of the multiple elements, including the creation unit, efficiency improvement unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the creation unit consolidates manuals and handover documents from each department using the control unit 46A of the robot 414 to create a unified knowledge base. The efficiency improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors usage logs and business processes of internal systems and tools, automatically updates the knowledge base, and optimizes procedures. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates manuals tailored to skill levels and creates training content using AI voice. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides a virtual help desk that directly responds to inquiries from residents.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The knowledge base platform may further include a feedback collection unit. The feedback collection unit may collect feedback from users and use it to improve the knowledge base. For example, the feedback collection unit may collect users' impressions and suggestions for improvement after using the manual and update the content of the knowledge base. The feedback collection unit may also evaluate the user's satisfaction when using the virtual help desk and improve the response method. Furthermore, the feedback collection unit may analyze user feedback and optimize the structure and content of the knowledge base. This makes it possible to continuously improve the knowledge base by reflecting user feedback.

[0107] The creation unit can estimate the user's emotions and customize the content of the knowledge base based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit can provide concise and to-the-point content. If the user is feeling relaxed, the creation unit can provide content with detailed explanations. If the user is feeling excited, the creation unit can provide visually appealing content. This allows for efficient work by customizing the content of the knowledge base based on the user's emotions.

[0108] The Efficiency Improvement Department can improve the accuracy of efficiency improvements by conducting detailed analysis of usage logs of internal systems and tools. For example, the Efficiency Improvement Department can prioritize optimization of frequently used functions based on usage logs. The Efficiency Improvement Department can also identify areas where errors frequently occur from usage logs and implement improvements. The Efficiency Improvement Department can also analyze usage logs to understand and optimize user behavior patterns. In this way, detailed analysis of usage logs of internal systems and tools improves the accuracy of optimization.

[0109] The generation unit can estimate the user's emotions and optimize the manual generation method based on the estimated user's emotions. For example, if the user is stressed, the generation unit can generate a concise manual that focuses on the main points. If the user is relaxed, the generation unit can generate a manual that includes detailed explanations. If the user is in a hurry, the generation unit can generate a manual that can be completed quickly. This allows for efficient work by adjusting the manual generation method based on the user's emotions.

[0110] When directly responding to inquiries from residents, the information providing unit can refer to past inquiry history to select an efficient response method. For example, the information providing unit can provide optimal answers to frequently asked questions based on the past inquiry history. The information providing unit can also suggest solutions to specific problems from the past inquiry history. The information providing unit can also analyze the past inquiry history and select the optimal response method. This allows for efficient work by selecting the optimal response method by referring to the past inquiry history.

[0111] When creating the knowledge base, the creation department can optimize the content by referring to best practices from other local governments. For example, the creation department can refer to successful cases from other local governments and reflect them in the knowledge base. The creation department can also improve business procedures based on the best practices of other local governments. The creation department can also enrich the content of the knowledge base by sharing information with other local governments. This makes it possible to create an efficient knowledge base by enriching the content by referring to best practices from other local governments.

[0112] The efficiency improvement unit can estimate the user's emotions and adjust the method for optimizing the procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the efficiency improvement unit can simplify the procedure to reduce the burden. If the user is relaxed, the efficiency improvement unit can provide detailed procedures to deepen understanding. If the user is in a hurry, the efficiency improvement unit can provide procedures that can be completed quickly. This allows for efficient work by adjusting the method for optimizing the procedure based on the user's emotions.

[0113] When automatically generating manuals tailored to skill levels, the generation unit can refer to past learning data to improve the efficiency of the generation algorithm. For example, the generation unit generates a manual for beginners based on past learning data. The generation unit can also generate advanced manuals for experts from past learning data. The generation unit can also analyze past learning data and select the optimal generation algorithm. This allows for efficient work by optimizing the generation algorithm by referring to past learning data.

[0114] The providing unit can estimate the user's emotions and optimize the virtual counter's response method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can respond in a gentle tone, providing a sense of security. If the user is relaxed, the providing unit can respond in a friendly manner, providing a sense of familiarity. If the user is in a hurry, the providing unit can respond quickly and concisely. This allows for efficient work by adjusting the virtual counter's response method based on the user's emotions.

[0115] When providing the virtual help desk, the provision unit can optimize the response content by taking into account the resident's attribute information. For example, the provision unit can provide an appropriate response method depending on the resident's age and gender. The provision unit can also provide an individually customized response based on the resident's past usage history. The provision unit can also provide the optimal response content by taking into account the resident's attribute information. This allows for efficient work by customizing the response content by taking into account the resident's attribute information.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The creation department creates a knowledge base. For example, the creation department consolidates the manuals and handover documents from each department and creates a unified knowledge base. Step 2: The Efficiency Division streamlines procedures based on the knowledge base created by the Creation Division. For example, the Efficiency Division monitors usage logs and business processes of internal systems and tools, automatically updating the knowledge base and optimizing procedures. Step 3: The generation unit automatically generates manuals based on the procedures streamlined by the efficiency unit. For example, the generation unit automatically generates manuals tailored to skill levels and creates training content using AI voice. Step 4: The providing unit provides a virtual counter based on the manual generated by the generating unit. For example, the providing unit provides a virtual counter that directly responds to inquiries from residents.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a creation unit that creates a knowledge base; an efficiency improvement unit that improves the efficiency of a procedure based on the knowledge base created by the creation unit; a generating unit that automatically generates a manual based on the procedure made more efficient by the efficiency improving unit; a providing unit that provides a virtual window based on the manual generated by the generating unit; Equipped with A system characterized by:

2. The creation unit Consolidate manuals and handover documents from each department to create an integrated knowledge base 2. The system of claim 1.

3. The efficiency improvement unit Monitor usage logs and business processes of internal systems and tools to automatically update knowledge bases and streamline procedures.

2. The system of claim 1.

4. The generation unit Automatically generate manuals tailored to skill levels and create training content using AI voice.

2. The system of claim 1.

5. The providing unit Providing a virtual point of contact to directly respond to residents' inquiries 2. The system of claim 1.

6. The creation unit Estimate user emotions and adjust the timing of knowledge base creation based on the estimated user emotions.

2. The system of claim 1.

7. The creation unit Analyze the business procedures of each department in detail and compile them into a common format, then determine priorities based on the importance of the work.

2. The system of claim 1.

8. The creation unit When consolidating manuals and handover documents from each department, select an efficient method of consolidation by referring to past work history.

2. The system of claim 1.

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