system

Abstract

The system according to this embodiment aims to automate the creation of procedure manuals and achieve efficient information transmission. [Solution] The system according to the embodiment comprises an analysis unit, a collection unit, a generation unit, and a provision unit. The analysis unit analyzes a flowchart. The collection unit collects detailed information based on the information of the flowchart analyzed by the analysis unit. The generation unit generates a procedure manual based on the information collected by the collection unit. The provision unit provides the procedure manual generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time and labor to manually create a procedure manual, and it is difficult to transmit information efficiently.

[0005] The system according to the embodiment aims to automate the creation of a procedure manual and realize efficient information transmission.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a data collection unit, a generation unit, and a provision unit. The analysis unit analyzes a flowchart. The data collection unit collects detailed information based on the information of the flowchart analyzed by the analysis unit. The generation unit generates a procedure manual based on the information collected by the data collection unit. The provision unit provides the procedure manual generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the creation of procedure manuals and achieve efficient information transmission. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent for automatic procedure manual generation according to an embodiment of the present invention is a system that analyzes a flowchart, collects necessary detailed information through dialogue with a generating AI, and automatically generates a procedure manual based on a pre-prepared format. This AI agent for automatic procedure manual generation understands the processes within a company and extracts appropriate content to quickly create a consistent manual. For example, the AI ​​agent for automatic procedure manual generation analyzes a flowchart. A flowchart is a visual representation of a business process and contains detailed information for each step. For example, in process management in the manufacturing industry, product quality control procedures and production line operation procedures are included in the flowchart. The AI ​​agent for automatic procedure manual generation analyzes this flowchart and extracts detailed information for each step. Next, the AI ​​agent for automatic procedure manual generation collects necessary detailed information through dialogue with a generating AI. The generating AI uses, for example, a conversational AI. The AI ​​agent for automatic procedure manual generation asks the generating AI for detailed information for each step, and collects the necessary information when the generating AI answers. For example, in a product quality control procedure, the agent asks the generating AI for details on specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The collected information is automatically generated as procedure manuals based on a pre-defined format. This format follows the standard procedure manual format used within the company, enabling the creation of consistent documentation. For example, in IT department procedure manuals, system installation procedures and troubleshooting procedures are written according to this format. This mechanism dramatically reduces the time required to create procedure manuals and significantly reduces the risk of errors. By utilizing a standardized format, documentation is standardized, improving transparency and consistency in operations. Information sharing across the company becomes smoother, employees can perform their tasks more efficiently, and productivity is increased. For example, operators can be provided with data analysis and quality control skills, aiming to improve product quality and productivity. As a result, the AI ​​agent for automatic procedure manual generation can automate the creation of procedure manuals and produce them efficiently.

[0029] The AI ​​agent for automatic procedure manual generation according to this embodiment comprises an analysis unit, a collection unit, a generation unit, and a provision unit. The analysis unit analyzes a flowchart. The analysis unit analyzes a flowchart that visually represents a business process and extracts detailed information for each step. For example, in process management in the manufacturing industry, the analysis unit can analyze product quality control procedures and production line operation procedures. The analysis unit analyzes each step of the flowchart in detail and extracts the necessary information. The collection unit collects detailed information based on the information of the flowchart analyzed by the analysis unit. The collection unit collects detailed information, for example, through dialogue with the generation AI. The collection unit asks the generation AI for detailed information on each step, and collects the necessary information when the generation AI answers. For example, in product quality control procedures, the collection unit asks the generation AI for details of specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The generation unit generates the procedure manual based on the information collected by the collection unit. The generation unit generates the procedure manual, for example, based on a pre-prepared format. The generation unit creates consistent documents in accordance with the standard procedure manual format used within the company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to the format. The delivery unit provides the procedure manuals generated by the generation unit. The delivery unit shares the generated procedure manuals within the company, for example. The delivery unit can also review the contents of the procedure manuals and make corrections as needed. The delivery unit manages updates to the procedure manuals and always provides the latest versions. As a result, the automated procedure manual generation AI agent according to this embodiment can automate the creation of procedure manuals and create them efficiently.

[0030] The analysis unit analyzes flowcharts. For example, the analysis unit analyzes flowcharts that visually represent business processes and extracts detailed information for each step. Specifically, the analysis unit uses natural language processing technology to identify each node and edge in the flowchart and understand what each step means. For example, in process management in the manufacturing industry, when analyzing product quality control procedures or production line operation procedures, the analysis unit analyzes the names and descriptions of each step to identify what kind of operation or inspection each step represents. The analysis unit uses a combination of image recognition technology and text analysis technology to analyze each step of the flowchart in detail and extract the necessary information. For example, it analyzes the flowchart image to identify the position and connection relationships of each step, and uses text analysis technology to extract specific operation content and conditions from the description of each step. In this way, the analysis unit can accurately understand the information in the flowchart and obtain the detailed information necessary for creating procedure manuals.

[0031] The data collection unit collects detailed information based on the flowchart information analyzed by the analysis unit. The data collection unit collects detailed information, for example, through interaction with the generating AI. Specifically, the data collection unit asks the generating AI for detailed information on each step, and collects the necessary information when the generating AI answers. For example, in a product quality control procedure, the data collection unit asks the generating AI for details on specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The data collection unit can use predefined question templates to ask the generating AI specific questions. For example, it can ask the generating AI questions such as, "What equipment is used in this step?" or "Please tell me the specific procedure for this inspection method." The generating AI generates answers to these questions by referring to its previously learned knowledge and database. The data collection unit organizes the answers obtained from the generating AI and incorporates them as the information necessary for the procedure manual. This allows the data collection unit to efficiently collect detailed information and comprehensively cover the information necessary for creating the procedure manual.

[0032] The generation unit generates procedure manuals based on information collected by the collection unit. The generation unit generates procedure manuals based on, for example, a pre-prepared format. Specifically, the generation unit creates consistent documents following the standard procedure manual format used within the company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to a format. The generation unit places the detailed information provided by the collection unit into the appropriate sections and clearly describes each step of the procedure manual. The generation unit uses natural language generation technology to ensure that the content of the procedure manual is consistent, easy to read, and easy to understand. For example, the generation unit describes each step of the procedure manual concisely and clearly, and inserts diagrams and tables as needed to aid in the visual understanding of the procedure manual. This allows the generation unit to generate high-quality procedure manuals based on the collected information, improving operational efficiency within the company.

[0033] The provisioning department provides the procedure manuals generated by the generation department. The provisioning department, for example, shares the generated procedure manuals within the company. Specifically, the provisioning department can review the content of the procedure manuals and make corrections as needed. The provisioning department uses a version control system to manage updates to the procedure manuals and ensure that the latest version is always provided. For example, the provisioning department records the change history for each version of the procedure manual and clarifies the changes by comparing them with previous versions. The provisioning department can also conduct expert reviews to ensure that the content of the procedure manuals is accurate. The provisioning department uploads the procedure manuals to the company's intranet or cloud storage so that relevant parties can access them at any time. This allows the provisioning department to efficiently share and manage the procedure manuals and promote information sharing within the company. Furthermore, the provisioning department can continuously improve the content of the procedure manuals by monitoring their usage and collecting feedback from users. This allows the provisioning department to always provide the latest and highest quality procedure manuals and improve operational efficiency within the company.

[0034] The data collection unit can collect detailed information through dialogue with the generating AI. For example, the data collection unit can collect necessary information by asking the generating AI detailed information about each step and receiving answers from the generating AI. For example, in a product quality control procedure, the data collection unit can collect information necessary for the procedure manual by asking the generating AI details about specific inspection methods and equipment used and receiving answers. This allows for the efficient collection of detailed information through dialogue with the generating AI. The dialogue with the generating AI is performed, for example, using a conversational AI. Some or all of the above-described processes in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input prompts to the generating AI asking for detailed information about each step, and the generating AI can generate answers.

[0035] The analysis unit can analyze each step of a flowchart in detail. For example, the analysis unit can analyze a flowchart that visually represents a business process and extract detailed information for each step. For example, in process management in the manufacturing industry, the analysis unit can analyze product quality control procedures and production line operation procedures. The analysis unit analyzes each step of the flowchart in detail and extracts the necessary information. By analyzing each step of the flowchart in detail, it is possible to generate accurate procedure manuals. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs prompts to the generation AI to analyze each step of the flowchart, and the generation AI generates the analysis results.

[0036] The generation unit can generate procedure manuals based on a pre-prepared format. For example, the generation unit creates consistent documents according to the format of a standard procedure manual used within a company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to the format. This ensures that consistent procedure manuals can be created by generating them based on a pre-prepared format. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to generate a procedure manual based on a pre-prepared format, and the generation AI generates the procedure manual.

[0037] The service provider can share the generated procedure manuals within the company. For example, the service provider can upload the generated procedure manuals to a shared folder or document management system within the company. The service provider can also review the content of the procedure manuals and make corrections as needed. The service provider manages the updates of the procedure manuals and always provides the latest version. This facilitates information sharing by sharing the generated procedure manuals within the company. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input a prompt to the generating AI to upload the generated procedure manual to a shared folder within the company, and the generating AI will upload the procedure manual.

[0038] The generation unit can review the contents of the procedure manual and make corrections as necessary. For example, the generation unit can review the contents of the generated procedure manual and check for errors or deficiencies. If there are errors or deficiencies in the contents of the procedure manual, the generation unit will make corrections as necessary. In this way, by reviewing the contents of the procedure manual and making corrections as necessary, an accurate procedure manual can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input a prompt to the generation AI to review the contents of the generated procedure manual, and the generation AI can review the contents of the procedure manual and make corrections as necessary.

[0039] The provisioning unit can manage the updates of the procedure manuals. For example, the provisioning unit manages the update history of the procedure manuals and always provides the latest procedure manuals. If the content of the procedure manuals is changed, the provisioning unit records the changes and manages the update history. In this way, by managing the updates of the procedure manuals, the latest procedure manuals can always be provided. Some or all of the above processes in the provisioning unit may be performed using AI or not. For example, the provisioning unit inputs prompts to the generating AI to manage the update history of the procedure manuals, and the generating AI manages the update history.

[0040] The data collection unit can ask the generating AI for detailed information about each step and obtain answers. For example, the data collection unit can generate prompts to ask the generating AI for detailed information about each step, and the generating AI can generate answers. For example, in a product quality control procedure, the data collection unit can ask the generating AI for details about specific inspection methods and equipment used, and obtain answers to collect the information necessary for the procedure manual. In this way, detailed information can be efficiently collected by asking questions to the generating AI and obtaining answers. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input prompts to the generating AI for detailed information about each step, and the generating AI can generate answers.

[0041] The analysis unit can optimize its analysis algorithm by referring to past analysis results when analyzing a flowchart. For example, the analysis unit can apply the most suitable analysis method to similar flowcharts based on past analysis results. The analysis unit can extract specific patterns from past analysis results and improve the analysis algorithm. The analysis unit can refer to past analysis results and take measures to prevent errors from occurring. In this way, by referring to past analysis results, the analysis algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit inputs a prompt to the generation AI to refer to past analysis results, and the generation AI optimizes the analysis algorithm.

[0042] The analysis unit can apply different analysis methods depending on the type of business process when analyzing flowcharts. For example, the analysis unit can apply an analysis method specialized for quality control to manufacturing processes. For IT business processes, it can apply an analysis method specialized for system operation. For service industry processes, it can apply an analysis method specialized for customer service. By applying an analysis method appropriate to the type of business process, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs a prompt to the generating AI to apply an analysis method appropriate to the type of business process, and the generating AI applies the analysis method.

[0043] The analysis unit can perform flowchart analysis while considering the geographical distribution of business processes. For example, the analysis unit can perform analysis considering regional characteristics for geographically dispersed business processes. The analysis unit can perform analysis considering common characteristics for geographically concentrated business processes. The analysis unit can select the optimal analysis method by considering geographical factors. This makes it possible to perform more appropriate analysis by considering the geographical distribution of business processes. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs a prompt to the generation AI to perform an analysis that considers the geographical distribution of business processes, and the generation AI performs the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant business documents when analyzing a flowchart. For example, the analysis unit can refer to relevant business documents to confirm the details of each step in the flowchart. The analysis unit can improve its analysis algorithm based on the information obtained from the business documents. The analysis unit can supplement the flowchart analysis results by referring to business documents. In this way, the accuracy of the analysis can be improved by referring to relevant business documents. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs a prompt to the generating AI to refer to relevant business documents, and the generating AI refers to the business documents to improve the accuracy of the analysis.

[0045] The data collection unit can optimize its collection algorithm by referring to past collected data when collecting detailed information. For example, the data collection unit can apply the most suitable collection method to similar information based on past collected data. The data collection unit can extract specific patterns from past collected data and improve its collection algorithm. The data collection unit can refer to past collected data and take measures to prevent errors. In this way, by referring to past collected data, the data collection algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to refer to past collected data, and the generative AI optimizes the data collection algorithm.

[0046] The data collection unit can apply different data collection methods depending on the type of business process when collecting detailed information. For example, the data collection unit can apply a data collection method specialized for quality control to manufacturing processes. For IT business processes, the data collection unit can apply a data collection method specialized for system operation. For service industry processes, the data collection unit can apply a data collection method specialized for customer service. By applying a data collection method appropriate to the type of business process, the accuracy of data collection can be improved. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to apply a data collection method appropriate to the type of business process, and the generative AI applies the data collection method.

[0047] The data collection unit can collect detailed information while considering the geographical distribution of business processes. For example, the data collection unit can collect information for geographically dispersed business processes while considering the characteristics of each region. For geographically concentrated business processes, the data collection unit can collect information while considering the common characteristics. The data collection unit can select the optimal data collection method while considering geographical factors. This makes it possible to collect more appropriate information by considering the geographical distribution of business processes. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a prompt to the generation AI to perform data collection while considering the geographical distribution of business processes, and the generation AI will perform the data collection.

[0048] The data collection unit can improve the accuracy of data collection by referring to relevant business documents when collecting detailed information. For example, the data collection unit can refer to relevant business documents to help collect detailed information. The data collection unit can improve the data collection algorithm based on the information obtained from the business documents. The data collection unit can supplement the collected information by referring to business documents. This allows the accuracy of data collection to be improved by referring to relevant business documents. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit inputs a prompt to the generating AI to refer to relevant business documents, and the generating AI refers to the business documents to improve the accuracy of data collection.

[0049] The generation unit can optimize its generation algorithm by referring to past generation data when generating procedure manuals. For example, the generation unit can apply the optimal generation method to similar procedure manuals based on past generation data. The generation unit can extract specific patterns from past generation data and improve the generation algorithm. The generation unit can refer to past generation data and take measures to prevent errors. In this way, by referring to past generation data, the generation algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to refer to past generation data, and the generation AI optimizes the generation algorithm.

[0050] The generation unit can apply different generation methods depending on the type of business process when generating procedure manuals. For example, the generation unit can apply a generation method specialized for quality control to manufacturing processes. For IT business processes, it can apply a generation method specialized for system operation. For service industry processes, it can apply a generation method specialized for customer service. By applying a generation method appropriate to the type of business process, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to apply a generation method appropriate to the type of business process, and the generation AI applies the generation method.

[0051] The generation unit can generate procedure manuals while considering the geographical distribution of business processes. For example, the generation unit can generate procedure manuals for geographically dispersed business processes while considering the characteristics of each region. The generation unit can generate procedure manuals for geographically concentrated business processes while considering the common characteristics. The generation unit can select the optimal generation method while considering geographical factors. This allows for the generation of more appropriate procedure manuals by considering the geographical distribution of business processes. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to perform generation considering the geographical distribution of business processes, and the generation AI performs the generation.

[0052] The generation unit can improve the accuracy of the procedure manual by referring to relevant business documents during the generation process. For example, the generation unit can refer to relevant business documents to confirm the details of each step in the procedure manual. The generation unit can improve the generation algorithm based on the information obtained from the business documents. The generation unit can refer to business documents to supplement the results of the procedure manual generation. In this way, the accuracy of the generation can be improved by referring to relevant business documents. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input a prompt to the generation AI to refer to relevant business documents, and the generation AI can refer to the business documents to improve the accuracy of the generation.

[0053] The service provider can optimize its service algorithm by referring to past service data when providing instructions. For example, the service provider can apply the optimal service method to similar instructions based on past service data. The service provider can extract specific patterns from past service data and improve its service algorithm. The service provider can refer to past service data and take measures to prevent errors. This allows the service provider to optimize its service algorithm and improve accuracy by referring to past service data. Some or all of the above processes in the service provider may be performed using a generative AI, or not. For example, the service provider can input a prompt to the generative AI to refer to past service data, and the generative AI can optimize its service algorithm.

[0054] The service provider can apply different service provision methods depending on the type of business process when providing procedure manuals. For example, the service provider can apply a service provision method specialized in quality control to manufacturing processes. For IT business processes, the service provider can apply a service provision method specialized in system operation. For service industry processes, the service provider can apply a service provision method specialized in customer service. By applying a service provision method appropriate to the type of business process, the accuracy of the service provision can be improved. Some or all of the above-described processes in the service provider may be performed using a generative AI, or they may be performed without a generative AI. For example, the service provider inputs a prompt to the generative AI to apply a service provision method appropriate to the type of business process, and the generative AI applies the service provision method.

[0055] The service provider can provide procedure manuals while considering the geographical distribution of business processes. For example, the service provider can provide procedure manuals for geographically dispersed business processes while considering the characteristics of each region. For geographically concentrated business processes, the service provider can provide procedure manuals while considering the common characteristics. The service provider can select the optimal delivery method while considering geographical factors. This allows for the provision of more appropriate procedure manuals by considering the geographical distribution of business processes. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input a prompt to the generation AI to provide a manual that considers the geographical distribution of business processes, and the generation AI will then provide the manual.

[0056] The service provider can improve the accuracy of its service provision by referring to relevant business documents when providing procedure manuals. For example, the service provider can refer to relevant business documents to confirm the details of each step in the procedure manual. The service provider can improve its service provision algorithm based on the information obtained from the business documents. The service provider can refer to business documents to supplement the results of the procedure manual provision. In this way, the accuracy of the service provision can be improved by referring to relevant business documents. Some or all of the above processes in the service provider may be performed using a generating AI, or not using a generating AI. For example, the service provider can input a prompt to the generating AI to refer to relevant business documents, and the generating AI can refer to the business documents to improve the accuracy of the service provision.

[0057] The service provider can collect user feedback when providing instructions and improve the service provision algorithm. For example, the service provider can improve the service provision method based on user feedback. The service provider can extract specific patterns from user feedback and improve the service provision algorithm. The service provider can refer to user feedback and take measures to prevent errors. In this way, by collecting user feedback, the service provision algorithm can be improved and accuracy can be increased. Some or all of the above processes in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input a prompt to the generative AI to collect user feedback, and the generative AI will collect the feedback and improve the service provision algorithm.

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

[0059] The analysis unit can optimize the analysis algorithm by referring to past analysis results when analyzing flowcharts. For example, it can apply the optimal analysis method to similar flowcharts based on past analysis results. It can extract specific patterns from past analysis results and improve the analysis algorithm. It can take measures to prevent errors by referring to past analysis results. In this way, by referring to past analysis results, the analysis algorithm can be optimized and accuracy can be improved.

[0060] The data collection unit can optimize its collection algorithm by referring to past collected data when collecting detailed information. For example, it can apply the most suitable collection method to similar information based on past collected data. It can extract specific patterns from past collected data and improve the collection algorithm. It can refer to past collected data and take measures to prevent errors. In this way, by referring to past collected data, the collection algorithm can be optimized and accuracy can be improved.

[0061] The generation unit can optimize its generation algorithm by referring to past generation data when generating procedure manuals. For example, it can apply the optimal generation method to similar procedure manuals based on past generation data. It can also extract specific patterns from past generation data and improve the generation algorithm. By referring to past generation data, it can take measures to prevent errors from occurring. In this way, by referring to past generation data, the generation algorithm can be optimized and accuracy can be improved.

[0062] The provisioning department can optimize its provisioning algorithm by referring to past provisioning data when providing procedure manuals. For example, it can apply the optimal provisioning method to similar procedure manuals based on past provisioning data. It can extract specific patterns from past provisioning data and improve the provisioning algorithm. It can refer to past provisioning data and take measures to prevent errors from occurring. In this way, by referring to past provisioning data, the provisioning algorithm can be optimized and accuracy can be improved.

[0063] The service provider can collect user feedback when providing instructions and improve the service provision algorithm. For example, they can improve the service provision method based on user feedback. They can extract specific patterns from user feedback and improve the service provision algorithm. They can refer to user feedback and take measures to prevent errors. In this way, by collecting user feedback, they can improve the service provision algorithm and increase accuracy.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis unit analyzes the flowchart. The analysis unit analyzes the flowchart, which visually represents the business process, and extracts detailed information for each step. For example, in process management in the manufacturing industry, it can analyze product quality control procedures and production line operation procedures. Step 2: The collection unit collects detailed information based on the flowchart information analyzed by the analysis unit. The collection unit collects detailed information through interaction with the generating AI. For example, in a product quality control procedure, the collection unit asks the generating AI questions about specific inspection methods and details of the equipment used, and collects the information necessary for the procedure manual by obtaining answers. Step 3: The generation unit generates procedure manuals based on the information collected by the collection unit. The generation unit generates procedure manuals based on a pre-prepared format. For example, when creating procedure manuals for the IT department, system installation procedures and troubleshooting procedures are written according to the format. Step 4: The providing department provides the procedure manuals generated by the generating department. The providing department can also share the generated procedure manuals within the company, review their contents, and make modifications as needed. The providing department manages the updates to the procedure manuals and ensures that the latest versions are always provided.

[0066] (Example of form 2) An AI agent for automatic procedure manual generation according to an embodiment of the present invention is a system that analyzes a flowchart, collects necessary detailed information through dialogue with a generating AI, and automatically generates a procedure manual based on a pre-prepared format. This AI agent for automatic procedure manual generation understands the processes within a company and extracts appropriate content to quickly create a consistent manual. For example, the AI ​​agent for automatic procedure manual generation analyzes a flowchart. A flowchart is a visual representation of a business process and contains detailed information for each step. For example, in process management in the manufacturing industry, product quality control procedures and production line operation procedures are included in the flowchart. The AI ​​agent for automatic procedure manual generation analyzes this flowchart and extracts detailed information for each step. Next, the AI ​​agent for automatic procedure manual generation collects necessary detailed information through dialogue with a generating AI. The generating AI uses, for example, a conversational AI. The AI ​​agent for automatic procedure manual generation asks the generating AI for detailed information for each step, and collects the necessary information when the generating AI answers. For example, in a product quality control procedure, the agent asks the generating AI for details on specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The collected information is automatically generated as procedure manuals based on a pre-defined format. This format follows the standard procedure manual format used within the company, enabling the creation of consistent documentation. For example, in IT department procedure manuals, system installation procedures and troubleshooting procedures are written according to this format. This mechanism dramatically reduces the time required to create procedure manuals and significantly reduces the risk of errors. By utilizing a standardized format, documentation is standardized, improving transparency and consistency in operations. Information sharing across the company becomes smoother, employees can perform their tasks more efficiently, and productivity is increased. For example, operators can be provided with data analysis and quality control skills, aiming to improve product quality and productivity. As a result, the AI ​​agent for automatic procedure manual generation can automate the creation of procedure manuals and produce them efficiently.

[0067] The AI ​​agent for automatic procedure manual generation according to this embodiment comprises an analysis unit, a collection unit, a generation unit, and a provision unit. The analysis unit analyzes a flowchart. The analysis unit analyzes a flowchart that visually represents a business process and extracts detailed information for each step. For example, in process management in the manufacturing industry, the analysis unit can analyze product quality control procedures and production line operation procedures. The analysis unit analyzes each step of the flowchart in detail and extracts the necessary information. The collection unit collects detailed information based on the information of the flowchart analyzed by the analysis unit. The collection unit collects detailed information, for example, through dialogue with the generation AI. The collection unit asks the generation AI for detailed information on each step, and collects the necessary information when the generation AI answers. For example, in product quality control procedures, the collection unit asks the generation AI for details of specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The generation unit generates the procedure manual based on the information collected by the collection unit. The generation unit generates the procedure manual, for example, based on a pre-prepared format. The generation unit creates consistent documents in accordance with the standard procedure manual format used within the company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to the format. The delivery unit provides the procedure manuals generated by the generation unit. The delivery unit shares the generated procedure manuals within the company, for example. The delivery unit can also review the contents of the procedure manuals and make corrections as needed. The delivery unit manages updates to the procedure manuals and always provides the latest versions. As a result, the automated procedure manual generation AI agent according to this embodiment can automate the creation of procedure manuals and create them efficiently.

[0068] The analysis unit analyzes flowcharts. For example, the analysis unit analyzes flowcharts that visually represent business processes and extracts detailed information for each step. Specifically, the analysis unit uses natural language processing technology to identify each node and edge in the flowchart and understand what each step means. For example, in process management in the manufacturing industry, when analyzing product quality control procedures or production line operation procedures, the analysis unit analyzes the names and descriptions of each step to identify what kind of operation or inspection each step represents. The analysis unit uses a combination of image recognition technology and text analysis technology to analyze each step of the flowchart in detail and extract the necessary information. For example, it analyzes the flowchart image to identify the position and connection relationships of each step, and uses text analysis technology to extract specific operation content and conditions from the description of each step. In this way, the analysis unit can accurately understand the information in the flowchart and obtain the detailed information necessary for creating procedure manuals.

[0069] The data collection unit collects detailed information based on the flowchart information analyzed by the analysis unit. The data collection unit collects detailed information, for example, through interaction with the generating AI. Specifically, the data collection unit asks the generating AI for detailed information on each step, and collects the necessary information when the generating AI answers. For example, in a product quality control procedure, the data collection unit asks the generating AI for details on specific inspection methods and equipment used, and collects the information necessary for the procedure manual by obtaining answers. The data collection unit can use predefined question templates to ask the generating AI specific questions. For example, it can ask the generating AI questions such as, "What equipment is used in this step?" or "Please tell me the specific procedure for this inspection method." The generating AI generates answers to these questions by referring to its previously learned knowledge and database. The data collection unit organizes the answers obtained from the generating AI and incorporates them as the information necessary for the procedure manual. This allows the data collection unit to efficiently collect detailed information and comprehensively cover the information necessary for creating the procedure manual.

[0070] The generation unit generates procedure manuals based on information collected by the collection unit. The generation unit generates procedure manuals based on, for example, a pre-prepared format. Specifically, the generation unit creates consistent documents following the standard procedure manual format used within the company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to a format. The generation unit places the detailed information provided by the collection unit into the appropriate sections and clearly describes each step of the procedure manual. The generation unit uses natural language generation technology to ensure that the content of the procedure manual is consistent, easy to read, and easy to understand. For example, the generation unit describes each step of the procedure manual concisely and clearly, and inserts diagrams and tables as needed to aid in the visual understanding of the procedure manual. This allows the generation unit to generate high-quality procedure manuals based on the collected information, improving operational efficiency within the company.

[0071] The provisioning department provides the procedure manuals generated by the generation department. The provisioning department, for example, shares the generated procedure manuals within the company. Specifically, the provisioning department can review the content of the procedure manuals and make corrections as needed. The provisioning department uses a version control system to manage updates to the procedure manuals and ensure that the latest version is always provided. For example, the provisioning department records the change history for each version of the procedure manual and clarifies the changes by comparing them with previous versions. The provisioning department can also conduct expert reviews to ensure that the content of the procedure manuals is accurate. The provisioning department uploads the procedure manuals to the company's intranet or cloud storage so that relevant parties can access them at any time. This allows the provisioning department to efficiently share and manage the procedure manuals and promote information sharing within the company. Furthermore, the provisioning department can continuously improve the content of the procedure manuals by monitoring their usage and collecting feedback from users. This allows the provisioning department to always provide the latest and highest quality procedure manuals and improve operational efficiency within the company.

[0072] The data collection unit can collect detailed information through dialogue with the generating AI. For example, the data collection unit can collect necessary information by asking the generating AI detailed information about each step and receiving answers from the generating AI. For example, in a product quality control procedure, the data collection unit can collect information necessary for the procedure manual by asking the generating AI details about specific inspection methods and equipment used and receiving answers. This allows for the efficient collection of detailed information through dialogue with the generating AI. The dialogue with the generating AI is performed, for example, using a conversational AI. Some or all of the above-described processes in the data collection unit may be performed using the generating AI or not. For example, the data collection unit can input prompts to the generating AI asking for detailed information about each step, and the generating AI can generate answers.

[0073] The analysis unit can analyze each step of a flowchart in detail. For example, the analysis unit can analyze a flowchart that visually represents a business process and extract detailed information for each step. For example, in process management in the manufacturing industry, the analysis unit can analyze product quality control procedures and production line operation procedures. The analysis unit analyzes each step of the flowchart in detail and extracts the necessary information. By analyzing each step of the flowchart in detail, it is possible to generate accurate procedure manuals. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs prompts to the generation AI to analyze each step of the flowchart, and the generation AI generates the analysis results.

[0074] The generation unit can generate procedure manuals based on a pre-prepared format. For example, the generation unit creates consistent documents according to the format of a standard procedure manual used within a company. For example, in creating procedure manuals for the IT department, the generation unit describes system installation procedures and troubleshooting procedures according to the format. This ensures that consistent procedure manuals can be created by generating them based on a pre-prepared format. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to generate a procedure manual based on a pre-prepared format, and the generation AI generates the procedure manual.

[0075] The service provider can share the generated procedure manuals within the company. For example, the service provider can upload the generated procedure manuals to a shared folder or document management system within the company. The service provider can also review the content of the procedure manuals and make corrections as needed. The service provider manages the updates of the procedure manuals and always provides the latest version. This facilitates information sharing by sharing the generated procedure manuals within the company. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input a prompt to the generating AI to upload the generated procedure manual to a shared folder within the company, and the generating AI will upload the procedure manual.

[0076] The generation unit can review the contents of the procedure manual and make corrections as necessary. For example, the generation unit can review the contents of the generated procedure manual and check for errors or deficiencies. If there are errors or deficiencies in the contents of the procedure manual, the generation unit will make corrections as necessary. In this way, by reviewing the contents of the procedure manual and making corrections as necessary, an accurate procedure manual can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input a prompt to the generation AI to review the contents of the generated procedure manual, and the generation AI can review the contents of the procedure manual and make corrections as necessary.

[0077] The provisioning unit can manage the updates of the procedure manuals. For example, the provisioning unit manages the update history of the procedure manuals and always provides the latest procedure manuals. If the content of the procedure manuals is changed, the provisioning unit records the changes and manages the update history. In this way, by managing the updates of the procedure manuals, the latest procedure manuals can always be provided. Some or all of the above processes in the provisioning unit may be performed using AI or not. For example, the provisioning unit inputs prompts to the generating AI to manage the update history of the procedure manuals, and the generating AI manages the update history.

[0078] The data collection unit can ask the generating AI for detailed information about each step and obtain answers. For example, the data collection unit can generate prompts to ask the generating AI for detailed information about each step, and the generating AI can generate answers. For example, in a product quality control procedure, the data collection unit can ask the generating AI for details about specific inspection methods and equipment used, and obtain answers to collect the information necessary for the procedure manual. In this way, detailed information can be efficiently collected by asking questions to the generating AI and obtaining answers. Some or all of the above processing in the data collection unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the data collection unit can input prompts to the generating AI for detailed information about each step, and the generating AI can generate answers.

[0079] The analysis unit can estimate the user's emotions and adjust the flowchart analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can simplify the analysis algorithm and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. If the user is in a hurry, the analysis unit can focus on the important steps. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and adjusts the analysis method.

[0080] The analysis unit can optimize its analysis algorithm by referring to past analysis results when analyzing a flowchart. For example, the analysis unit can apply the most suitable analysis method to similar flowcharts based on past analysis results. The analysis unit can extract specific patterns from past analysis results and improve the analysis algorithm. The analysis unit can refer to past analysis results and take measures to prevent errors from occurring. In this way, by referring to past analysis results, the analysis algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit inputs a prompt to the generation AI to refer to past analysis results, and the generation AI optimizes the analysis algorithm.

[0081] The analysis unit can apply different analysis methods depending on the type of business process when analyzing flowcharts. For example, the analysis unit can apply an analysis method specialized for quality control to manufacturing processes. For IT business processes, it can apply an analysis method specialized for system operation. For service industry processes, it can apply an analysis method specialized for customer service. By applying an analysis method appropriate to the type of business process, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs a prompt to the generating AI to apply an analysis method appropriate to the type of business process, and the generating AI applies the analysis method.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and adjusts the display method.

[0083] The analysis unit can perform flowchart analysis while considering the geographical distribution of business processes. For example, the analysis unit can perform analysis considering regional characteristics for geographically dispersed business processes. The analysis unit can perform analysis considering common characteristics for geographically concentrated business processes. The analysis unit can select the optimal analysis method by considering geographical factors. This makes it possible to perform more appropriate analysis by considering the geographical distribution of business processes. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs a prompt to the generation AI to perform an analysis that considers the geographical distribution of business processes, and the generation AI performs the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant business documents when analyzing a flowchart. For example, the analysis unit can refer to relevant business documents to confirm the details of each step in the flowchart. The analysis unit can improve its analysis algorithm based on the information obtained from the business documents. The analysis unit can supplement the flowchart analysis results by referring to business documents. In this way, the accuracy of the analysis can be improved by referring to relevant business documents. Some or all of the above processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit inputs a prompt to the generating AI to refer to relevant business documents, and the generating AI refers to the business documents to improve the accuracy of the analysis.

[0085] The data collection unit can estimate the user's emotions and adjust the method of collecting detailed information based on the estimated emotions. For example, if the user is stressed, the data collection unit can ask concise questions and quickly collect information. If the user is relaxed, the data collection unit can ask detailed questions and collect more information. If the user is in a hurry, the data collection unit can ask questions that focus on important information. This allows for more appropriate information collection by adjusting the collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and adjusts the collection method.

[0086] The data collection unit can optimize its collection algorithm by referring to past collected data when collecting detailed information. For example, the data collection unit can apply the most suitable collection method to similar information based on past collected data. The data collection unit can extract specific patterns from past collected data and improve its collection algorithm. The data collection unit can refer to past collected data and take measures to prevent errors. In this way, by referring to past collected data, the data collection algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to refer to past collected data, and the generative AI optimizes the data collection algorithm.

[0087] The data collection unit can apply different data collection methods depending on the type of business process when collecting detailed information. For example, the data collection unit can apply a data collection method specialized for quality control to manufacturing processes. For IT business processes, the data collection unit can apply a data collection method specialized for system operation. For service industry processes, the data collection unit can apply a data collection method specialized for customer service. By applying a data collection method appropriate to the type of business process, the accuracy of data collection can be improved. Some or all of the above-described processes in the data collection unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to apply a data collection method appropriate to the type of business process, and the generative AI applies the data collection method.

[0088] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit can prioritize collecting detailed information. If the user is in a hurry, the data collection unit can prioritize information that can be collected quickly. This allows for more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and determines the priority of information.

[0089] The data collection unit can collect detailed information while considering the geographical distribution of business processes. For example, the data collection unit can collect information for geographically dispersed business processes while considering the characteristics of each region. For geographically concentrated business processes, the data collection unit can collect information while considering the common characteristics. The data collection unit can select the optimal data collection method while considering geographical factors. This makes it possible to collect more appropriate information by considering the geographical distribution of business processes. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input a prompt to the generation AI to perform data collection while considering the geographical distribution of business processes, and the generation AI will perform the data collection.

[0090] The data collection unit can improve the accuracy of data collection by referring to relevant business documents when collecting detailed information. For example, the data collection unit can refer to relevant business documents to help collect detailed information. The data collection unit can improve the data collection algorithm based on the information obtained from the business documents. The data collection unit can supplement the collected information by referring to business documents. This allows the accuracy of data collection to be improved by referring to relevant business documents. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit inputs a prompt to the generating AI to refer to relevant business documents, and the generating AI refers to the business documents to improve the accuracy of data collection.

[0091] The generation unit can estimate the user's emotions and adjust the method of generating the instruction manual based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an instruction manual that proceeds at a relaxed pace. If the user is in a hurry, the generation unit can generate an instruction manual that emphasizes the shortest route. If the user is excited, the generation unit can generate an instruction manual with visually stimulating effects. In this way, by adjusting the generation method according to the user's emotions, a more appropriate instruction manual can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not using the generation AI. For example, the generation unit inputs a prompt to the generation AI to estimate the user's emotions, the generation AI estimates the emotions, and adjusts the generation method.

[0092] The generation unit can optimize its generation algorithm by referring to past generation data when generating procedure manuals. For example, the generation unit can apply the optimal generation method to similar procedure manuals based on past generation data. The generation unit can extract specific patterns from past generation data and improve the generation algorithm. The generation unit can refer to past generation data and take measures to prevent errors. In this way, by referring to past generation data, the generation algorithm can be optimized and accuracy can be improved. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to refer to past generation data, and the generation AI optimizes the generation algorithm.

[0093] The generation unit can apply different generation methods depending on the type of business process when generating procedure manuals. For example, the generation unit can apply a generation method specialized for quality control to manufacturing processes. For IT business processes, it can apply a generation method specialized for system operation. For service industry processes, it can apply a generation method specialized for customer service. By applying a generation method appropriate to the type of business process, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to apply a generation method appropriate to the type of business process, and the generation AI applies the generation method.

[0094] The generation unit can estimate the user's emotions and determine the priority of the instructions to be generated based on the estimated emotions. For example, if the user is nervous, the generation unit may prioritize generating important instructions. If the user is relaxed, the generation unit may prioritize generating detailed instructions. If the user is in a hurry, the generation unit may prioritize instructions that can be generated quickly. This allows for the generation of more appropriate instructions by prioritizing instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generation AI. For example, the generation unit inputs a prompt to the generation AI to estimate the user's emotions, the generation AI estimates the emotions, and determines the priority of the instructions.

[0095] The generation unit can generate procedure manuals while considering the geographical distribution of business processes. For example, the generation unit can generate procedure manuals for geographically dispersed business processes while considering the characteristics of each region. The generation unit can generate procedure manuals for geographically concentrated business processes while considering the common characteristics. The generation unit can select the optimal generation method while considering geographical factors. This allows for the generation of more appropriate procedure manuals by considering the geographical distribution of business processes. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs a prompt to the generation AI to perform generation considering the geographical distribution of business processes, and the generation AI performs the generation.

[0096] The generation unit can improve the accuracy of the procedure manual by referring to relevant business documents during the generation process. For example, the generation unit can refer to relevant business documents to confirm the details of each step in the procedure manual. The generation unit can improve the generation algorithm based on the information obtained from the business documents. The generation unit can refer to business documents to supplement the results of the procedure manual generation. In this way, the accuracy of the generation can be improved by referring to relevant business documents. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input a prompt to the generation AI to refer to relevant business documents, and the generation AI can refer to the business documents to improve the accuracy of the generation.

[0097] The delivery unit can estimate the user's emotions and adjust the method of delivering the instructions based on the estimated emotions. For example, if the user is nervous, the delivery unit can provide a simple and highly visible method of delivery. If the user is relaxed, the delivery unit can provide a method that includes detailed information. If the user is in a hurry, the delivery unit can provide a concise method of delivery. By adjusting the delivery method according to the user's emotions, a more appropriate instruction manual can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and adjusts the delivery method.

[0098] The service provider can optimize its service algorithm by referring to past service data when providing instructions. For example, the service provider can apply the optimal service method to similar instructions based on past service data. The service provider can extract specific patterns from past service data and improve its service algorithm. The service provider can refer to past service data and take measures to prevent errors. This allows the service provider to optimize its service algorithm and improve accuracy by referring to past service data. Some or all of the above processes in the service provider may be performed using a generative AI, or not. For example, the service provider can input a prompt to the generative AI to refer to past service data, and the generative AI can optimize its service algorithm.

[0099] The service provider can apply different service provision methods depending on the type of business process when providing procedure manuals. For example, the service provider can apply a service provision method specialized in quality control to manufacturing processes. For IT business processes, the service provider can apply a service provision method specialized in system operation. For service industry processes, the service provider can apply a service provision method specialized in customer service. By applying a service provision method appropriate to the type of business process, the accuracy of the service provision can be improved. Some or all of the above-described processes in the service provider may be performed using a generative AI, or they may be performed without a generative AI. For example, the service provider inputs a prompt to the generative AI to apply a service provision method appropriate to the type of business process, and the generative AI applies the service provision method.

[0100] The service provider can estimate the user's emotions and determine the priority of the instructions to be provided based on the estimated emotions. For example, if the user is nervous, the service provider may prioritize providing important instructions. If the user is relaxed, the service provider may prioritize providing detailed instructions. If the user is in a hurry, the service provider may prioritize instructions that can be provided quickly. This allows for the provision of more appropriate instructions by prioritizing instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs a prompt to the generative AI to estimate the user's emotions, the generative AI estimates the emotions, and determines the priority of the instructions.

[0101] The service provider can provide procedure manuals while considering the geographical distribution of business processes. For example, the service provider can provide procedure manuals for geographically dispersed business processes while considering the characteristics of each region. For geographically concentrated business processes, the service provider can provide procedure manuals while considering the common characteristics. The service provider can select the optimal delivery method while considering geographical factors. This allows for the provision of more appropriate procedure manuals by considering the geographical distribution of business processes. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input a prompt to the generation AI to provide a manual that considers the geographical distribution of business processes, and the generation AI will then provide the manual.

[0102] The service provider can improve the accuracy of its service provision by referring to relevant business documents when providing procedure manuals. For example, the service provider can refer to relevant business documents to confirm the details of each step in the procedure manual. The service provider can improve its service provision algorithm based on the information obtained from the business documents. The service provider can refer to business documents to supplement the results of the procedure manual provision. In this way, the accuracy of the service provision can be improved by referring to relevant business documents. Some or all of the above processes in the service provider may be performed using a generating AI, or not using a generating AI. For example, the service provider can input a prompt to the generating AI to refer to relevant business documents, and the generating AI can refer to the business documents to improve the accuracy of the service provision.

[0103] The service provider can collect user feedback when providing instructions and improve the service provision algorithm. For example, the service provider can improve the service provision method based on user feedback. The service provider can extract specific patterns from user feedback and improve the service provision algorithm. The service provider can refer to user feedback and take measures to prevent errors. In this way, by collecting user feedback, the service provision algorithm can be improved and accuracy can be increased. Some or all of the above processes in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input a prompt to the generative AI to collect user feedback, and the generative AI will collect the feedback and improve the service provision algorithm.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible.

[0106] The data collection unit can estimate the user's emotions and adjust how detailed information is collected based on those emotions. For example, if the user is stressed, concise questions can be asked to quickly gather information. If the user is relaxed, more detailed questions can be asked to gather more information. If the user is in a hurry, questions can be asked that focus on important information. By adjusting the data collection method according to the user's emotions, more appropriate information can be collected.

[0107] The generation unit can estimate the user's emotions and adjust the method of generating the instruction manual based on those emotions. For example, if the user is relaxed, it can generate an instruction manual that proceeds at a leisurely pace. If the user is in a hurry, it can generate an instruction manual that emphasizes the shortest route. If the user is excited, it can generate an instruction manual with visually stimulating effects. In this way, by adjusting the generation method according to the user's emotions, it is possible to generate more appropriate instruction manuals.

[0108] The delivery unit can estimate the user's emotions and adjust the way the instructions are delivered based on those emotions. For example, if the user is nervous, a simple and highly visible delivery method can be provided. If the user is relaxed, a delivery method including detailed information can be provided. If the user is in a hurry, a delivery method that gets straight to the point can be provided. In this way, by adjusting the delivery method according to the user's emotions, more appropriate instructions can be provided.

[0109] The delivery unit can estimate the user's emotions and determine the priority of the instructions to be provided based on those emotions. For example, if the user is nervous, important instructions can be prioritized. If the user is relaxed, detailed instructions can be prioritized. If the user is in a hurry, instructions that can be provided quickly can be prioritized. In this way, by prioritizing instructions according to the user's emotions, more appropriate instructions can be provided.

[0110] The analysis unit can optimize the analysis algorithm by referring to past analysis results when analyzing flowcharts. For example, it can apply the optimal analysis method to similar flowcharts based on past analysis results. It can extract specific patterns from past analysis results and improve the analysis algorithm. It can take measures to prevent errors by referring to past analysis results. In this way, by referring to past analysis results, the analysis algorithm can be optimized and accuracy can be improved.

[0111] The data collection unit can optimize its collection algorithm by referring to past collected data when collecting detailed information. For example, it can apply the most suitable collection method to similar information based on past collected data. It can extract specific patterns from past collected data and improve the collection algorithm. It can refer to past collected data and take measures to prevent errors. In this way, by referring to past collected data, the collection algorithm can be optimized and accuracy can be improved.

[0112] The generation unit can optimize its generation algorithm by referring to past generation data when generating procedure manuals. For example, it can apply the optimal generation method to similar procedure manuals based on past generation data. It can also extract specific patterns from past generation data and improve the generation algorithm. By referring to past generation data, it can take measures to prevent errors from occurring. In this way, by referring to past generation data, the generation algorithm can be optimized and accuracy can be improved.

[0113] The provisioning department can optimize its provisioning algorithm by referring to past provisioning data when providing procedure manuals. For example, it can apply the optimal provisioning method to similar procedure manuals based on past provisioning data. It can extract specific patterns from past provisioning data and improve the provisioning algorithm. It can refer to past provisioning data and take measures to prevent errors from occurring. In this way, by referring to past provisioning data, the provisioning algorithm can be optimized and accuracy can be improved.

[0114] The service provider can collect user feedback when providing instructions and improve the service provision algorithm. For example, they can improve the service provision method based on user feedback. They can extract specific patterns from user feedback and improve the service provision algorithm. They can refer to user feedback and take measures to prevent errors. In this way, by collecting user feedback, they can improve the service provision algorithm and increase accuracy.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The analysis unit analyzes the flowchart. The analysis unit analyzes the flowchart, which visually represents the business process, and extracts detailed information for each step. For example, in process management in the manufacturing industry, it can analyze product quality control procedures and production line operation procedures. Step 2: The collection unit collects detailed information based on the flowchart information analyzed by the analysis unit. The collection unit collects detailed information through interaction with the generating AI. For example, in a product quality control procedure, the collection unit asks the generating AI questions about specific inspection methods and details of the equipment used, and collects the information necessary for the procedure manual by obtaining answers. Step 3: The generation unit generates procedure manuals based on the information collected by the collection unit. The generation unit generates procedure manuals based on a pre-prepared format. For example, when creating procedure manuals for the IT department, system installation procedures and troubleshooting procedures are written according to the format. Step 4: The providing department provides the procedure manuals generated by the generating department. The providing department can also share the generated procedure manuals within the company, review their contents, and make modifications as needed. The providing department manages the updates to the procedure manuals and ensures that the latest versions are always provided.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the analysis unit, collection unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14, which analyzes the flowchart and extracts detailed information for each step. The collection unit is implemented by the specific processing unit 290 of the data processing device 12, which collects detailed information through interaction with the generation AI. The generation unit is implemented by the control unit 46A of the smart device 14, which generates a procedure manual based on the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, which provides the generated procedure manual. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the analysis unit, collection unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the flowchart and extracts detailed information for each step. The collection unit is implemented by the specific processing unit 290 of the data processing device 12, which collects detailed information through interaction with the generating AI. The generation unit is implemented by the control unit 46A of the smart glasses 214, which generates a procedure manual based on the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, which provides the generated procedure manual. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, collection unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the flowchart and extracts detailed information for each step. The collection unit is implemented by the specific processing unit 290 of the data processing device 12, which collects detailed information through interaction with the generation AI. The generation unit is implemented by the control unit 46A of the headset terminal 314, which generates a procedure manual based on the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12, which provides the generated procedure manual. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the analysis unit, collection unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which analyzes the flowchart and extracts detailed information for each step. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, which collects detailed information through interaction with the generation AI. The generation unit is implemented by the control unit 46A of the robot 414, which generates a procedure manual based on the collected information. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides the generated procedure manual. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) An analysis unit that analyzes flowcharts, A collection unit that collects detailed information based on the flowchart information analyzed by the aforementioned analysis unit, A generation unit generates a procedure manual based on the information collected by the collection unit, The system includes a providing unit that provides the procedure manual generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather detailed information through interaction with the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze each step of the flowchart in detail. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate procedure manuals based on a pre-prepared format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Share the generated procedure manual within the company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Review the instructions and make any necessary corrections. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Manage the update of procedure manuals The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We ask the generating AI for detailed information about each step and obtain answers. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the flowchart analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing flowcharts, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing flowcharts, different analysis methods are applied depending on the type of business process. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing flowcharts, the analysis should take into account the geographical distribution of business processes. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing flowcharts, referencing relevant business documents improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is We estimate the user's emotions and adjust how we collect detailed information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting detailed information, the collection algorithm is optimized by referring to past collected data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting detailed information, different collection methods are applied depending on the type of business process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is When collecting detailed information, the geographical distribution of business processes should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When collecting detailed information, refer to relevant business documents to improve the accuracy of the collection. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and adjusts the method of generating the instruction manual based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating procedure manuals, the generation algorithm is optimized by referring to past generation data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating procedure manuals, different generation methods are applied depending on the type of business process. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and determines the priority of the instructions to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating procedure manuals, the geographical distribution of business processes should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating procedure manuals, refer to related business documents to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the method of providing instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the procedure manual, we optimize the provision algorithm by referring to past provision data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing procedure manuals, different delivery methods will be applied depending on the type of business process. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates the user's emotions and determines the priority of the instructions to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing procedural manuals, consider the geographical distribution of business processes. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing procedure manuals, we improve the accuracy of the manuals by referring to related business documents. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing instructions, we collect user feedback and improve the algorithms we provide. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes flowcharts, A collection unit that collects detailed information based on the flowchart information analyzed by the aforementioned analysis unit, A generation unit generates a procedure manual based on the information collected by the collection unit, The system includes a providing unit that provides the procedure manual generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather detailed information through interaction with the generating AI. The system according to feature 1.

3. The aforementioned analysis unit, Analyze each step of the flowchart in detail. The system according to feature 1.

4. The generating unit is Generate procedure manuals based on a pre-prepared format. The system according to feature 1.

5. The aforementioned supply unit is, Share the generated procedure manual within the company. The system according to feature 1.

6. The generating unit is Review the instructions and make any necessary corrections. The system according to feature 1.

7. The aforementioned supply unit is, Manage the update of procedure manuals The system according to feature 1.

8. The aforementioned collection unit is We ask the generating AI for detailed information about each step and obtain answers. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the flowchart analysis method based on the estimated user emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing flowcharts, the analysis algorithm is optimized by referring to past analysis results. The system according to feature 1.

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