system

Abstract

The system according to this embodiment aims to streamline the handover of tasks and provide manuals tailored to the successor's level of understanding. [Solution] The system according to this embodiment comprises a manual creation unit, a comprehension level determination unit, and a QA response unit. The manual creation unit generates a manual by inputting business steps. The comprehension level determination unit customizes the manual generated by the manual creation unit to match the comprehension level of the successor. The QA response unit performs QA based on the manual customized by the comprehension level determination unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that manual creation becomes personalized when taking over a business, and it is difficult to respond according to the understanding level of the successor.

[0005] The system according to the embodiment aims to streamline the business handover and provide a manual according to the understanding level of the successor.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a manual creation unit, a comprehension level determination unit, and a QA response unit. The manual creation unit generates a manual by inputting business steps. The comprehension level determination unit customizes the manual generated by the manual creation unit to match the comprehension level of the successor. The QA response unit performs QA based on the manual customized by the comprehension level determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the handover of tasks and provide manuals tailored to the successor's level of understanding. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business handover support system according to an embodiment of the present invention is a system that improves efficiency by solving the challenges of business handover using a generating AI. This business handover support system provides a tool that allows users to create a manual by filling in the necessary information according to the steps, and builds a mechanism that can present the manual in a way that matches the successor's level of understanding. In addition, simple initial answers to Q&A questions can be handled by the AI. For example, the business handover support system automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. Next, the business handover support system builds a mechanism that can present the manual in a way that matches the successor's level of understanding. For example, if the successor is asked "Are you familiar with this operation?" and answers "Yes, I am," the detailed explanation for that part is omitted. On the other hand, if the successor is asked "Do you have experience with this task?" and answers "No, I don't," a detailed explanation for that part is added. In this way, a manual is provided that matches the successor's level of knowledge. Furthermore, the business handover support system will also enable AI to handle simple initial answers to questions and answers. For example, if a successor asks, "I don't understand how to do this," the AI ​​will automatically provide an answer. This reduces inquiries to the predecessor and improves work efficiency. This mechanism ensures a smooth and efficient handover of duties. The predecessor can reduce the time spent creating manuals, and the successor can quickly learn the job by using manuals tailored to their level of understanding. In addition, the burden of handling inquiries is reduced by AI-powered Q&A support. In this way, the business handover support system can solve the challenges of business handover using AI generation and improve efficiency.

[0029] The business handover support system according to this embodiment comprises a manual creation unit, a comprehension assessment unit, and a QA support unit. The manual creation unit generates a manual by inputting business steps. For example, the manual creation unit automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. For example, if the comprehension assessment unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," the detailed explanation for that part is omitted. On the other hand, if the successor answers, "Do you have experience with this work?" and the successor answers, "No, I don't," a detailed explanation for that part is added. In this way, a manual tailored to the successor's knowledge level is provided. The QA support unit provides QA support based on the manual customized by the comprehension assessment unit. For example, if the successor asks, "I don't understand how to do this operation," the AI ​​automatically provides an answer. This reduces inquiries to predecessors and improves work efficiency. As a result, the business handover support system according to this embodiment can solve the challenges of business handover using AI generation and improve efficiency.

[0030] The manual creation unit generates manuals by inputting business steps. For example, the manual creation unit automatically generates a manual when a user inputs each step of a business process. Specifically, as the user inputs each step of the business process in order, the system analyzes the input and constructs the manual in an appropriate format. For example, the user might first input "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business process in order. Furthermore, the manual creation unit also has a function to automatically insert diagrams and screenshots for the inputted steps. For example, for the step "Extract data," it can automatically insert a screenshot showing the data extraction procedure. The manual creation unit can also adjust the level of detail of the steps according to the complexity of the business process. For example, it provides a concise explanation for simple tasks and detailed procedures for complex tasks. In this way, the manual creation unit can automatically generate efficient and effective manuals simply by the user inputting each step of the business process. In addition, the manual creation unit also has a function to output the generated manual in PDF or HTML format, allowing users to print the manual or share it on the web as needed. This makes the manual creation department a powerful tool for ensuring a smooth handover of tasks.

[0031] The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. Specifically, the comprehension assessment unit asks the successor a series of questions and adjusts the manual's content based on their answers. For example, if the successor is asked, "Do you know about this operation?" and answers, "Yes, I do," the detailed explanation for that section is omitted. On the other hand, if the successor is asked, "Do you have experience with this task?" and answers, "No, I don't," a detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. Furthermore, the comprehension assessment unit can score the successor's understanding of specific work steps based on their answers and dynamically adjust the manual's content according to that score. For example, if the successor scores low on a particular work step, a detailed explanation or supplementary material for that step is added. The comprehension assessment unit can also monitor the successor's learning progress and provide additional training or resources as needed. For example, if the successor repeatedly asks questions about a particular work step, a detailed video tutorial or interactive training module for that step is provided. In this way, the comprehension assessment unit provides a customized manual tailored to the successor's level of understanding, enabling an efficient and effective handover of duties.

[0032] The QA support unit provides QA support based on manuals customized by the comprehension assessment unit. Specifically, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. For example, if a successor inputs a question about a specific work step, the QA support unit analyzes the question, searches the relevant manual section and supplementary materials, and provides an appropriate answer. The AI ​​uses natural language processing technology to understand the intent of the question and generates the optimal answer. For example, if asked, "I don't understand how to extract data," the AI ​​extracts the relevant part of the manual and provides specific steps and screenshots. The QA support unit can also learn from past question and answer history to provide more accurate answers. Furthermore, the QA support unit evaluates the accuracy and satisfaction of the answers to the successor's questions and continuously improves them. For example, if the successor provides feedback on the answers provided, the AI ​​can improve the quality of the answers based on that feedback. The QA support unit can also prioritize and allocate resources to efficiently handle multiple questions that arise simultaneously. This allows the QA support department to support the successor in smoothly carrying out their duties and reduce inquiries to the predecessor, thereby improving operational efficiency.

[0033] The business step input section generates a manual by inputting business steps. For example, the business step input section automatically generates a manual when the user inputs each step of the business. For example, the user might first input "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. This makes it easier to input business steps and streamlines manual creation. Some or all of the above processing in the business step input section may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input section can input the business steps entered by the user into a generation AI, and the generation AI can automatically generate a manual.

[0034] The comprehension assessment unit includes a comprehension questioning unit that asks questions to determine the successor's level of understanding. For example, if the comprehension questioning unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," then the detailed explanation for that part is omitted. On the other hand, if the successor answers, "Do you have experience with this task?" and the successor answers, "No, I don't," then the detailed explanation for that part is added. In this way, a manual tailored to the successor's knowledge level is provided. This makes it possible to provide a manual that matches the successor's level of understanding. Some or all of the above processing in the comprehension questioning unit may be performed using AI or not. For example, the comprehension questioning unit can input the successor's answers into the AI, which can then automatically determine the level of understanding.

[0035] The QA support unit includes an answer generation unit that automatically generates answers to questions from successors. For example, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. This allows for a quick response to questions from successors. Some or all of the above-described processes in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input the successor's question into the AI, and the AI ​​can automatically generate an answer.

[0036] The manual creation unit automatically generates a manual by inputting the business steps. For example, the manual creation unit automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. This means that the manual is automatically generated simply by inputting the business steps. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the business steps entered by the user into a generation AI, and the generation AI can automatically generate the manual.

[0037] The comprehension assessment unit customizes the manual content according to the successor's level of understanding. For example, if the comprehension assessment unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," then the detailed explanation for that section is omitted. On the other hand, if the successor answers, "Do you have experience with this task?" and the successor answers, "No, I don't," then the detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. This ensures that a manual is provided that matches the successor's level of understanding. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the successor's answers into the AI, which can then automatically determine the level of understanding and customize the manual content.

[0038] The manual creation unit automatically completes the input of business steps by referring to manuals for similar past tasks. For example, when a user enters "data extraction," the manual creation unit automatically completes the "data extraction procedure" from manuals for similar past tasks. The manual creation unit can also automatically complete the past report creation procedure when a user enters "report creation." Furthermore, the manual creation unit can automatically complete the past meeting preparation procedure when a user enters "meeting preparation." This reduces the effort required for input by referring to manuals for similar past tasks. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the business steps entered by the user into a generation AI, which can then automatically complete the input by referring to manuals for similar past tasks.

[0039] The manual creation unit adjusts the level of detail in the manual based on the importance of the task when the task steps are entered. For example, the manual creation unit creates manuals that include detailed procedures and points to note for high-importance task steps. It can also create manuals that include only concise procedures for low-importance task steps. Furthermore, it can create manuals that include a balanced mix of necessary procedures and supplementary information for moderately important task steps. This allows for the provision of detailed manuals tailored to the importance of each task. Some or all of the above processes in the manual creation unit may be performed using a generation AI, or not. For example, the manual creation unit can input the task steps entered by the user into a generation AI, which can then adjust the level of detail in the manual based on the importance of the task.

[0040] The manual creation unit, when inputting business steps, refers to the user's business history to suggest the optimal steps. For example, the manual creation unit suggests the optimal steps based on the business steps the user has performed in the past. The manual creation unit can also prioritize suggesting frequently performed steps based on the user's business history. Furthermore, the manual creation unit can analyze the user's business history and suggest efficient steps. This allows for the suggestion of optimal steps based on the user's business history. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the user's business history data into a generation AI, which can then suggest the optimal steps.

[0041] The manual creation unit customizes the manual content based on the user's work environment when inputting business steps. For example, if the user is working remotely, the manual creation unit creates a manual suitable for the remote environment. It can also create a manual suitable for the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the manual creation unit can create a manual suitable for the fieldwork environment. This allows for the provision of manuals tailored to the user's work environment. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the user's work environment data into the generation AI, which can then customize the manual content.

[0042] The comprehension assessment unit adjusts the difficulty level of questions by referring to the user's past answer history when assessing comprehension. For example, the comprehension assessment unit increases the difficulty level of questions the user has answered correctly in the past. It can also decrease the difficulty level of questions the user has answered incorrectly in the past. Furthermore, the comprehension assessment unit can analyze the user's past answer history and provide questions of appropriate difficulty. This allows the unit to provide questions of appropriate difficulty based on the user's past answer history. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's past answer history data into an AI, which can then adjust the difficulty level of the questions.

[0043] The comprehension assessment unit customizes the content of questions based on the user's work experience when assessing comprehension. For example, the comprehension assessment unit provides questions related to tasks in which the user has extensive experience. It can also provide basic questions related to tasks in which the user has no experience. Furthermore, the comprehension assessment unit can provide appropriate questions based on the user's work experience. This allows the comprehension level to be assessed with questions tailored to the user's work experience. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's work experience data into an AI, which can then customize the question content.

[0044] The comprehension assessment unit selects the most appropriate questions by referring to the user's learning history when assessing comprehension. For example, the comprehension assessment unit selects questions based on content the user has previously learned. The comprehension assessment unit can also provide questions related to areas where the user's comprehension is low, based on the user's learning history. Furthermore, the comprehension assessment unit can analyze the user's learning history and provide appropriate questions. This allows the unit to provide appropriate questions based on the user's learning history. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's learning history data into an AI, which can then select the most appropriate questions.

[0045] The comprehension assessment unit customizes the content of questions based on the user's work environment when assessing comprehension. For example, if the user is working remotely, the comprehension assessment unit will provide questions related to the remote work environment. It can also provide questions related to the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the comprehension assessment unit can provide questions related to the fieldwork environment. This allows the comprehension level to be assessed with questions tailored to the user's work environment. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's work environment data into the AI, which can then customize the questions.

[0046] The QA support unit improves the accuracy of its answers by referring to past question history during QA support. For example, the QA support unit provides appropriate answers based on the content of questions the user has asked in the past. The QA support unit can also provide answers to frequently asked questions based on the user's past question history. Furthermore, the QA support unit can analyze the user's past question history to provide highly accurate answers. This enables the provision of highly accurate answers based on past question history. Some or all of the above processing in the QA support unit may be performed using AI or not. For example, the QA support unit can input the user's past question history data into AI, which can then improve the accuracy of its answers.

[0047] The QA support unit prioritizes answers based on when the questions were submitted. For example, the QA support unit provides quick answers to urgent questions. It can also respond to regular questions within a standard response time. Furthermore, the QA support unit can lower the priority of questions that have been submitted in the past. This allows for the provision of answers with priorities according to when the questions were submitted. Some or all of the above processes in the QA support unit may be performed using AI or not. For example, the QA support unit can input question submission date data into an AI, which can then determine the priority of the answers.

[0048] The QA response unit adjusts the order of answers based on the relevance of the questions during QA. For example, the QA response unit prioritizes providing answers to highly relevant questions. It can also postpone providing answers to less relevant questions. Furthermore, the QA response unit can analyze the relevance of questions and provide answers in the optimal order. This allows answers to be provided in an order that corresponds to the relevance of the questions. Some or all of the above processing in the QA response unit may be performed using AI or not. For example, the QA response unit can input question relevance data into the AI, which can then adjust the order of answers.

[0049] The business step input unit suggests the optimal input method when a business step is entered, by referring to past input history. For example, the business step input unit automatically displays business steps that the user has frequently entered in the past as candidates. The business step input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the business step input unit can predict and suggest business steps to be used during a specific time period based on the user's past input history. This allows the optimal input method to be suggested based on past input history. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's past input history data into a generation AI, which can then suggest the optimal input method.

[0050] The business step input section adjusts the level of detail in the input based on the importance of the business step. For example, the business step input section provides detailed input fields for high-importance business steps. It can also provide concise input fields for low-importance business steps. Furthermore, it can provide a balanced combination of necessary input fields and supplementary information for business steps of moderate importance. This allows for detailed input according to the importance of the business. Some or all of the above processing in the business step input section may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input section can input the business steps entered by the user into a generation AI, which can then adjust the level of detail in the input based on the importance of the business.

[0051] The business step input unit suggests the optimal steps when a business step is entered, by referring to the user's business history. For example, the business step input unit suggests the optimal steps based on the business steps the user has performed in the past. The business step input unit can also prioritize suggesting frequently performed steps from the user's business history. Furthermore, the business step input unit can analyze the user's business history and suggest efficient steps. This allows the system to suggest the optimal steps based on the user's business history. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's business history data into a generation AI, which can then suggest the optimal steps.

[0052] The business step input unit customizes the input content based on the user's work environment when a business step is entered. For example, if the user is working remotely, the business step input unit provides input content suitable for the remote environment. It can also provide input content suitable for the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the business step input unit can provide input content suitable for the fieldwork environment. This allows for the provision of input content tailored to the user's work environment. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's work environment data into a generation AI, which can then customize the input content.

[0053] The comprehension question unit adjusts the difficulty of questions by referring to the user's past answer history. For example, the comprehension question unit increases the difficulty of questions the user has answered correctly in the past. It can also decrease the difficulty of questions the user has answered incorrectly in the past. Furthermore, the comprehension question unit can analyze the user's past answer history and provide questions of appropriate difficulty. This allows the unit to provide questions of appropriate difficulty based on past answer history. Some or all of the above processing in the comprehension question unit may be performed using AI or not. For example, the comprehension question unit can input the user's past answer history data into an AI, which can then adjust the difficulty of the questions.

[0054] The comprehension questioning unit selects the most appropriate question by referring to the user's learning history when asking comprehension questions. For example, the comprehension questioning unit selects a question based on what the user has learned in the past. The comprehension questioning unit can also provide questions related to areas where the user's comprehension is low, based on the user's learning history. Furthermore, the comprehension questioning unit can analyze the user's learning history and provide appropriate questions. This allows the unit to provide appropriate questions based on the user's learning history. Some or all of the above processing in the comprehension questioning unit may be performed using AI or not. For example, the comprehension questioning unit can input the user's learning history data into an AI, which can then select the most appropriate question.

[0055] The answer generation unit improves the accuracy of its answers by referring to past question history when generating answers. For example, the answer generation unit provides appropriate answers based on the content of questions the user has asked in the past. The answer generation unit can also provide answers to frequently asked questions based on the user's past question history. Furthermore, the answer generation unit can analyze the user's past question history and provide highly accurate answers. This enables the provision of highly accurate answers based on past question history. Some or all of the above processing in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input the user's past question history data into AI, which can then improve the accuracy of its answers.

[0056] The answer generation unit determines the priority of answers based on when the questions were submitted. For example, the answer generation unit provides quick answers to urgent questions. It can also respond to regular questions within a standard response time. Furthermore, the answer generation unit can lower the priority of questions that have been submitted in the past. This allows for the provision of answers with a priority order according to when the questions were submitted. Some or all of the above processing in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input question submission time data into the AI, which can then determine the priority of the answers.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The business handover support system can also include a business history analysis unit. This unit analyzes the user's past business history and proposes the optimal handover procedure. For example, it can determine the handover priority based on the frequency and importance of tasks the user has performed in the past. Furthermore, the business history analysis unit can automatically generate detailed procedures for specific tasks from the user's past business history. It can also analyze the user's business history and propose efficient handover procedures. This allows the system to provide the optimal handover procedure based on the user's business history.

[0059] The business handover support system can also include a feedback collection unit. This unit collects feedback from the successor and uses it to improve the manual. For example, the feedback collection unit allows the successor to evaluate the manual's content. It also allows the successor to suggest improvements to the manual. Furthermore, the feedback collection unit can automatically update the manual's content based on the successor's feedback. This allows for improvement of the manual's quality based on the successor's feedback.

[0060] The business handover support system can also be equipped with a business environment adaptation unit. This unit customizes the handover procedure based on the user's work environment. For example, if the user is working remotely, the unit provides a handover procedure suitable for a remote environment. It can also provide a handover procedure suitable for an office environment if the user is working in an office. Furthermore, if the user is performing on-site work, the unit can provide a handover procedure suitable for the on-site environment. This allows the system to provide handover procedures tailored to the user's specific work environment.

[0061] The business handover support system can also include a learning history analysis unit. This unit analyzes the user's learning history and proposes the optimal handover procedure. For example, it customizes the handover procedure based on the user's past learning. It can also provide detailed instructions for areas where the user's understanding is weak, based on their learning history. Furthermore, it can analyze the user's learning history and propose an efficient handover procedure. This allows the system to provide the optimal handover procedure based on the user's learning history.

[0062] The business handover support system can also be equipped with a business step prediction unit. This unit predicts the next business step to be performed based on the user's business history. For example, it analyzes patterns of past business steps performed by the user and proposes the next step. Furthermore, it can prioritize and suggest frequently performed steps based on the user's business history. Additionally, it can analyze the user's business history and propose efficient business steps. This allows the system to provide the optimal business steps based on the user's business history.

[0063] The business handover support system can also be equipped with an automated business step completion function. This function automatically completes business steps based on the business steps entered by the user, referencing manuals for similar past tasks. For example, when the user enters "data extraction," the automated business step completion function will automatically complete the "data extraction procedure" from manuals for similar past tasks. It can also automatically complete the report creation procedure when the user enters "report creation." Furthermore, it can automatically complete the meeting preparation procedure when the user enters "meeting preparation." This reduces the effort required for data entry by referencing manuals for similar past tasks.

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

[0065] Step 1: The manual creation section generates the manual by inputting the business steps. For example, the manual is automatically generated when the user inputs each step of the business. Specifically, the user first inputs "Extract data," then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. Step 2: The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. For example, if the successor is asked, "Do you know about this operation?" and answers, "Yes, I do," the detailed explanation for that section is omitted. On the other hand, if the successor is asked, "Do you have experience with this task?" and answers, "No, I don't," the detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. Step 3: The QA support unit provides QA support based on a manual customized by the comprehension assessment unit. For example, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. This reduces inquiries to the predecessor and improves work efficiency.

[0066] (Example of form 2) The business handover support system according to an embodiment of the present invention is a system that improves efficiency by solving the challenges of business handover using a generating AI. This business handover support system provides a tool that allows users to create a manual by filling in the necessary information according to the steps, and builds a mechanism that can present the manual in a way that matches the successor's level of understanding. In addition, simple initial answers to Q&A questions can be handled by the AI. For example, the business handover support system automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. Next, the business handover support system builds a mechanism that can present the manual in a way that matches the successor's level of understanding. For example, if the successor is asked "Are you familiar with this operation?" and answers "Yes, I am," the detailed explanation for that part is omitted. On the other hand, if the successor is asked "Do you have experience with this task?" and answers "No, I don't," a detailed explanation for that part is added. In this way, a manual is provided that matches the successor's level of knowledge. Furthermore, the business handover support system will also enable AI to handle simple initial answers to questions and answers. For example, if a successor asks, "I don't understand how to do this," the AI ​​will automatically provide an answer. This reduces inquiries to the predecessor and improves work efficiency. This mechanism ensures a smooth and efficient handover of duties. The predecessor can reduce the time spent creating manuals, and the successor can quickly learn the job by using manuals tailored to their level of understanding. In addition, the burden of handling inquiries is reduced by AI-powered Q&A support. In this way, the business handover support system can solve the challenges of business handover using AI generation and improve efficiency.

[0067] The business handover support system according to this embodiment comprises a manual creation unit, a comprehension assessment unit, and a QA support unit. The manual creation unit generates a manual by inputting business steps. For example, the manual creation unit automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. For example, if the comprehension assessment unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," the detailed explanation for that part is omitted. On the other hand, if the successor answers, "Do you have experience with this work?" and the successor answers, "No, I don't," a detailed explanation for that part is added. In this way, a manual tailored to the successor's knowledge level is provided. The QA support unit provides QA support based on the manual customized by the comprehension assessment unit. For example, if the successor asks, "I don't understand how to do this operation," the AI ​​automatically provides an answer. This reduces inquiries to predecessors and improves work efficiency. As a result, the business handover support system according to this embodiment can solve the challenges of business handover using AI generation and improve efficiency.

[0068] The manual creation unit generates manuals by inputting business steps. For example, the manual creation unit automatically generates a manual when a user inputs each step of a business process. Specifically, as the user inputs each step of the business process in order, the system analyzes the input and constructs the manual in an appropriate format. For example, the user might first input "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business process in order. Furthermore, the manual creation unit also has a function to automatically insert diagrams and screenshots for the inputted steps. For example, for the step "Extract data," it can automatically insert a screenshot showing the data extraction procedure. The manual creation unit can also adjust the level of detail of the steps according to the complexity of the business process. For example, it provides a concise explanation for simple tasks and detailed procedures for complex tasks. In this way, the manual creation unit can automatically generate efficient and effective manuals simply by the user inputting each step of the business process. In addition, the manual creation unit also has a function to output the generated manual in PDF or HTML format, allowing users to print the manual or share it on the web as needed. This makes the manual creation department a powerful tool for ensuring a smooth handover of tasks.

[0069] The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. Specifically, the comprehension assessment unit asks the successor a series of questions and adjusts the manual's content based on their answers. For example, if the successor is asked, "Do you know about this operation?" and answers, "Yes, I do," the detailed explanation for that section is omitted. On the other hand, if the successor is asked, "Do you have experience with this task?" and answers, "No, I don't," a detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. Furthermore, the comprehension assessment unit can score the successor's understanding of specific work steps based on their answers and dynamically adjust the manual's content according to that score. For example, if the successor scores low on a particular work step, a detailed explanation or supplementary material for that step is added. The comprehension assessment unit can also monitor the successor's learning progress and provide additional training or resources as needed. For example, if the successor repeatedly asks questions about a particular work step, a detailed video tutorial or interactive training module for that step is provided. In this way, the comprehension assessment unit provides a customized manual tailored to the successor's level of understanding, enabling an efficient and effective handover of duties.

[0070] The QA support unit provides QA support based on manuals customized by the comprehension assessment unit. Specifically, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. For example, if a successor inputs a question about a specific work step, the QA support unit analyzes the question, searches the relevant manual section and supplementary materials, and provides an appropriate answer. The AI ​​uses natural language processing technology to understand the intent of the question and generates the optimal answer. For example, if asked, "I don't understand how to extract data," the AI ​​extracts the relevant part of the manual and provides specific steps and screenshots. The QA support unit can also learn from past question and answer history to provide more accurate answers. Furthermore, the QA support unit evaluates the accuracy and satisfaction of the answers to the successor's questions and continuously improves them. For example, if the successor provides feedback on the answers provided, the AI ​​can improve the quality of the answers based on that feedback. The QA support unit can also prioritize and allocate resources to efficiently handle multiple questions that arise simultaneously. This allows the QA support department to support the successor in smoothly carrying out their duties and reduce inquiries to the predecessor, thereby improving operational efficiency.

[0071] The business step input section generates a manual by inputting business steps. For example, the business step input section automatically generates a manual when the user inputs each step of the business. For example, the user might first input "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. This makes it easier to input business steps and streamlines manual creation. Some or all of the above processing in the business step input section may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input section can input the business steps entered by the user into a generation AI, and the generation AI can automatically generate a manual.

[0072] The comprehension assessment unit includes a comprehension questioning unit that asks questions to determine the successor's level of understanding. For example, if the comprehension questioning unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," then the detailed explanation for that part is omitted. On the other hand, if the successor answers, "Do you have experience with this task?" and the successor answers, "No, I don't," then the detailed explanation for that part is added. In this way, a manual tailored to the successor's knowledge level is provided. This makes it possible to provide a manual that matches the successor's level of understanding. Some or all of the above processing in the comprehension questioning unit may be performed using AI or not. For example, the comprehension questioning unit can input the successor's answers into the AI, which can then automatically determine the level of understanding.

[0073] The QA support unit includes an answer generation unit that automatically generates answers to questions from successors. For example, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. This allows for a quick response to questions from successors. Some or all of the above-described processes in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input the successor's question into the AI, and the AI ​​can automatically generate an answer.

[0074] The manual creation unit automatically generates a manual by inputting the business steps. For example, the manual creation unit automatically generates a manual when the user inputs each step of the business. For example, the user first inputs "Extract data," and then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. This means that the manual is automatically generated simply by inputting the business steps. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the business steps entered by the user into a generation AI, and the generation AI can automatically generate the manual.

[0075] The comprehension assessment unit customizes the manual content according to the successor's level of understanding. For example, if the comprehension assessment unit asks the successor, "Do you know about this operation?" and the successor answers, "Yes, I do," then the detailed explanation for that section is omitted. On the other hand, if the successor answers, "Do you have experience with this task?" and the successor answers, "No, I don't," then the detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. This ensures that a manual is provided that matches the successor's level of understanding. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the successor's answers into the AI, which can then automatically determine the level of understanding and customize the manual content.

[0076] The manual creation unit estimates the user's emotions and adjusts the manual's presentation based on those emotions. For example, if the user is stressed, the manual creation unit creates a manual using concise and intuitive language. If the user is relaxed, the manual creation unit can also create a manual with detailed explanations and supplementary information. Furthermore, if the user is in a hurry, the manual creation unit can create a short, to-the-point manual. This allows the manual to be delivered in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the manual creation unit may be performed using or without generative AI. For example, the manual creation unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the manual's presentation accordingly.

[0077] The manual creation unit automatically completes the input of business steps by referring to manuals for similar past tasks. For example, when a user enters "data extraction," the manual creation unit automatically completes the "data extraction procedure" from manuals for similar past tasks. The manual creation unit can also automatically complete the past report creation procedure when a user enters "report creation." Furthermore, the manual creation unit can automatically complete the past meeting preparation procedure when a user enters "meeting preparation." This reduces the effort required for input by referring to manuals for similar past tasks. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the business steps entered by the user into a generation AI, which can then automatically complete the input by referring to manuals for similar past tasks.

[0078] The manual creation unit adjusts the level of detail in the manual based on the importance of the task when the task steps are entered. For example, the manual creation unit creates manuals that include detailed procedures and points to note for high-importance task steps. It can also create manuals that include only concise procedures for low-importance task steps. Furthermore, it can create manuals that include a balanced mix of necessary procedures and supplementary information for moderately important task steps. This allows for the provision of detailed manuals tailored to the importance of each task. Some or all of the above processes in the manual creation unit may be performed using a generation AI, or not. For example, the manual creation unit can input the task steps entered by the user into a generation AI, which can then adjust the level of detail in the manual based on the importance of the task.

[0079] The manual creation unit estimates the user's emotions and adjusts the length of the manual based on the estimated emotions. For example, if the user is stressed, the manual creation unit will create a short, concise manual. If the user is relaxed, the manual creation unit can also create a longer manual with detailed explanations. Furthermore, if the user is in a hurry, the manual creation unit can create a concise and quickly understandable manual. This allows for the provision of manuals of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 manual creation unit may be performed using or without generative AI. For example, the manual creation unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the length of the manual.

[0080] The manual creation unit, when inputting business steps, refers to the user's business history to suggest the optimal steps. For example, the manual creation unit suggests the optimal steps based on the business steps the user has performed in the past. The manual creation unit can also prioritize suggesting frequently performed steps based on the user's business history. Furthermore, the manual creation unit can analyze the user's business history and suggest efficient steps. This allows for the suggestion of optimal steps based on the user's business history. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the user's business history data into a generation AI, which can then suggest the optimal steps.

[0081] The manual creation unit customizes the manual content based on the user's work environment when inputting business steps. For example, if the user is working remotely, the manual creation unit creates a manual suitable for the remote environment. It can also create a manual suitable for the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the manual creation unit can create a manual suitable for the fieldwork environment. This allows for the provision of manuals tailored to the user's work environment. Some or all of the above processing in the manual creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the manual creation unit can input the user's work environment data into the generation AI, which can then customize the manual content.

[0082] The comprehension assessment unit estimates the user's emotions and adjusts the content of the comprehension assessment questions based on the estimated emotions. For example, if the user is nervous, the comprehension assessment unit provides simple and easy-to-answer questions. If the user is relaxed, the comprehension assessment unit can also provide questions to measure detailed comprehension. Furthermore, if the user is in a hurry, the comprehension assessment unit can provide questions that can be answered quickly. This allows the comprehension level to be determined with questions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the question content.

[0083] The comprehension assessment unit adjusts the difficulty level of questions by referring to the user's past answer history when assessing comprehension. For example, the comprehension assessment unit increases the difficulty level of questions the user has answered correctly in the past. It can also decrease the difficulty level of questions the user has answered incorrectly in the past. Furthermore, the comprehension assessment unit can analyze the user's past answer history and provide questions of appropriate difficulty. This allows the unit to provide questions of appropriate difficulty based on the user's past answer history. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's past answer history data into an AI, which can then adjust the difficulty level of the questions.

[0084] The comprehension assessment unit customizes the content of questions based on the user's work experience when assessing comprehension. For example, the comprehension assessment unit provides questions related to tasks in which the user has extensive experience. It can also provide basic questions related to tasks in which the user has no experience. Furthermore, the comprehension assessment unit can provide appropriate questions based on the user's work experience. This allows the comprehension level to be assessed with questions tailored to the user's work experience. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's work experience data into an AI, which can then customize the question content.

[0085] The comprehension assessment unit estimates the user's emotions and adjusts the order of questions based on the estimated emotions. For example, if the comprehension assessment unit is nervous, it may start with easy questions. Conversely, if the user is relaxed, it may start with more difficult questions. Furthermore, if the user is in a hurry, the comprehension assessment unit may prioritize providing important questions. This allows questions to be provided in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the order of questions.

[0086] The comprehension assessment unit selects the most appropriate questions by referring to the user's learning history when assessing comprehension. For example, the comprehension assessment unit selects questions based on content the user has previously learned. The comprehension assessment unit can also provide questions related to areas where the user's comprehension is low, based on the user's learning history. Furthermore, the comprehension assessment unit can analyze the user's learning history and provide appropriate questions. This allows the unit to provide appropriate questions based on the user's learning history. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's learning history data into an AI, which can then select the most appropriate questions.

[0087] The comprehension assessment unit customizes the content of questions based on the user's work environment when assessing comprehension. For example, if the user is working remotely, the comprehension assessment unit will provide questions related to the remote work environment. It can also provide questions related to the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the comprehension assessment unit can provide questions related to the fieldwork environment. This allows the comprehension level to be assessed with questions tailored to the user's work environment. Some or all of the above processing in the comprehension assessment unit may be performed using AI or not. For example, the comprehension assessment unit can input the user's work environment data into the AI, which can then customize the questions.

[0088] The QA response unit estimates the user's emotions and adjusts the way it expresses its response based on the estimated emotions. For example, if the user is stressed, the QA response unit provides a concise and intuitive response. If the user is relaxed, it can also provide a response that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a short, to-the-point response. This allows the response to be expressed in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the QA response unit may be performed using AI or not. For example, the QA response unit can input user emotion data into an AI, which can automatically estimate the emotions and adjust the way it expresses its response.

[0089] The QA support unit improves the accuracy of its answers by referring to past question history during QA support. For example, the QA support unit provides appropriate answers based on the content of questions the user has asked in the past. The QA support unit can also provide answers to frequently asked questions based on the user's past question history. Furthermore, the QA support unit can analyze the user's past question history to provide highly accurate answers. This enables the provision of highly accurate answers based on past question history. Some or all of the above processing in the QA support unit may be performed using AI or not. For example, the QA support unit can input the user's past question history data into AI, which can then improve the accuracy of its answers.

[0090] The QA response unit estimates the user's emotions and adjusts the length of the response based on the estimated emotions. For example, if the user is stressed, the QA response unit provides a short, to-the-point response. If the user is relaxed, the QA response unit may provide a longer response with more detailed explanations. Furthermore, if the user is in a hurry, the QA response unit may provide a concise, quickly understandable response. This allows for the provision of responses of appropriate length to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the QA response unit may be performed using AI or not. For example, the QA response unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the length of the response.

[0091] The QA support unit prioritizes answers based on when the questions were submitted. For example, the QA support unit provides quick answers to urgent questions. It can also respond to regular questions within a standard response time. Furthermore, the QA support unit can lower the priority of questions that have been submitted in the past. This allows for the provision of answers with priorities according to when the questions were submitted. Some or all of the above processes in the QA support unit may be performed using AI or not. For example, the QA support unit can input question submission date data into an AI, which can then determine the priority of the answers.

[0092] The QA response unit adjusts the order of answers based on the relevance of the questions during QA. For example, the QA response unit prioritizes providing answers to highly relevant questions. It can also postpone providing answers to less relevant questions. Furthermore, the QA response unit can analyze the relevance of questions and provide answers in the optimal order. This allows answers to be provided in an order that corresponds to the relevance of the questions. Some or all of the above processing in the QA response unit may be performed using AI or not. For example, the QA response unit can input question relevance data into the AI, which can then adjust the order of answers.

[0093] The business step input unit estimates the user's emotions and adjusts the input method for the business step based on the estimated emotions. For example, if the user is stressed, the business step input unit provides a simple and intuitive input method. If the user is relaxed, it can also provide detailed input options. Furthermore, if the user is in a hurry, the business step input unit can prioritize voice input to allow for quick input of the business step. This allows the business step to be provided with an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the business step input unit may be performed using or without a generative AI. For example, the business step input unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the input method.

[0094] The business step input unit suggests the optimal input method when a business step is entered, by referring to past input history. For example, the business step input unit automatically displays business steps that the user has frequently entered in the past as candidates. The business step input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the business step input unit can predict and suggest business steps to be used during a specific time period based on the user's past input history. This allows the optimal input method to be suggested based on past input history. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's past input history data into a generation AI, which can then suggest the optimal input method.

[0095] The business step input section adjusts the level of detail in the input based on the importance of the business step. For example, the business step input section provides detailed input fields for high-importance business steps. It can also provide concise input fields for low-importance business steps. Furthermore, it can provide a balanced combination of necessary input fields and supplementary information for business steps of moderate importance. This allows for detailed input according to the importance of the business. Some or all of the above processing in the business step input section may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input section can input the business steps entered by the user into a generation AI, which can then adjust the level of detail in the input based on the importance of the business.

[0096] The task step input unit estimates the user's emotions and determines the priority of the task steps to be entered based on the estimated emotions. For example, if the user is stressed, the task step input unit will prioritize important task steps. If the user is relaxed, the task step input unit can also input detailed task steps in order. Furthermore, if the user is in a hurry, the task step input unit can prioritize task steps that can be completed quickly. This allows for the provision of task steps with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 task step input unit may be performed using or without a generative AI. For example, the task step input unit can input user emotion data into a generative AI, which can automatically estimate the emotions and determine the priority of the task steps.

[0097] The business step input unit suggests the optimal steps when a business step is entered, by referring to the user's business history. For example, the business step input unit suggests the optimal steps based on the business steps the user has performed in the past. The business step input unit can also prioritize suggesting frequently performed steps from the user's business history. Furthermore, the business step input unit can analyze the user's business history and suggest efficient steps. This allows the system to suggest the optimal steps based on the user's business history. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's business history data into a generation AI, which can then suggest the optimal steps.

[0098] The business step input unit customizes the input content based on the user's work environment when a business step is entered. For example, if the user is working remotely, the business step input unit provides input content suitable for the remote environment. It can also provide input content suitable for the office environment if the user is working in the office. Furthermore, if the user is performing fieldwork, the business step input unit can provide input content suitable for the fieldwork environment. This allows for the provision of input content tailored to the user's work environment. Some or all of the above processing in the business step input unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the business step input unit can input the user's work environment data into a generation AI, which can then customize the input content.

[0099] The comprehension question unit estimates the user's emotions and adjusts the wording of the questions based on the estimated emotions. For example, if the user is nervous, the comprehension question unit provides simple and easy-to-answer questions. If the user is relaxed, the comprehension question unit can also provide questions to measure detailed comprehension. Furthermore, if the user is in a hurry, the comprehension question unit can provide questions that can be answered quickly. This allows questions to be presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comprehension question unit may be performed using AI or not. For example, the comprehension question unit can input user emotion data into an AI, which can automatically estimate the emotions and adjust the wording of the questions.

[0100] The comprehension question unit adjusts the difficulty of questions by referring to the user's past answer history. For example, the comprehension question unit increases the difficulty of questions the user has answered correctly in the past. It can also decrease the difficulty of questions the user has answered incorrectly in the past. Furthermore, the comprehension question unit can analyze the user's past answer history and provide questions of appropriate difficulty. This allows the unit to provide questions of appropriate difficulty based on past answer history. Some or all of the above processing in the comprehension question unit may be performed using AI or not. For example, the comprehension question unit can input the user's past answer history data into an AI, which can then adjust the difficulty of the questions.

[0101] The comprehension questioning unit estimates the user's emotions and adjusts the order of questions based on the estimated emotions. For example, if the comprehension questioning unit is nervous, it may start with easy questions. Conversely, if the user is relaxed, it may start with more difficult questions. Furthermore, if the user is in a hurry, it may prioritize providing important questions. This allows questions to be presented in an order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comprehension questioning unit may be performed using AI or not. For example, the comprehension questioning unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the order of questions.

[0102] The comprehension questioning unit selects the most appropriate question by referring to the user's learning history when asking comprehension questions. For example, the comprehension questioning unit selects a question based on what the user has learned in the past. The comprehension questioning unit can also provide questions related to areas where the user's comprehension is low, based on the user's learning history. Furthermore, the comprehension questioning unit can analyze the user's learning history and provide appropriate questions. This allows the unit to provide appropriate questions based on the user's learning history. Some or all of the above processing in the comprehension questioning unit may be performed using AI or not. For example, the comprehension questioning unit can input the user's learning history data into an AI, which can then select the most appropriate question.

[0103] The response generation unit estimates the user's emotions and adjusts the expression of the response based on the estimated emotions. For example, if the user is stressed, the response generation unit provides a concise and intuitive response. If the user is relaxed, the response generation unit can also provide a response that includes detailed explanations. Furthermore, if the user is in a hurry, the response generation unit can provide a short, to-the-point response. This allows the response to be expressed in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using AI or not. For example, the response generation unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the expression of the response.

[0104] The answer generation unit improves the accuracy of its answers by referring to past question history when generating answers. For example, the answer generation unit provides appropriate answers based on the content of questions the user has asked in the past. The answer generation unit can also provide answers to frequently asked questions based on the user's past question history. Furthermore, the answer generation unit can analyze the user's past question history and provide highly accurate answers. This enables the provision of highly accurate answers based on past question history. Some or all of the above processing in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input the user's past question history data into AI, which can then improve the accuracy of its answers.

[0105] The response generation unit estimates the user's emotions and adjusts the length of the response based on the estimated emotions. For example, if the user is stressed, the response generation unit provides a short, to-the-point response. If the user is relaxed, the response generation unit can also provide a longer response with more detailed explanations. Furthermore, if the user is in a hurry, the response generation unit can provide a concise and quickly understandable response. This allows for the provision of responses of appropriate length to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response generation unit may be performed using AI or not. For example, the response generation unit can input user emotion data into an AI, which can then automatically estimate the emotions and adjust the length of the response.

[0106] The answer generation unit determines the priority of answers based on when the questions were submitted. For example, the answer generation unit provides quick answers to urgent questions. It can also respond to regular questions within a standard response time. Furthermore, the answer generation unit can lower the priority of questions that have been submitted in the past. This allows for the provision of answers with a priority order according to when the questions were submitted. Some or all of the above processing in the answer generation unit may be performed using AI or not. For example, the answer generation unit can input question submission time data into the AI, which can then determine the priority of the answers.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The business handover support system can also include a business history analysis unit. This unit analyzes the user's past business history and proposes the optimal handover procedure. For example, it can determine the handover priority based on the frequency and importance of tasks the user has performed in the past. Furthermore, the business history analysis unit can automatically generate detailed procedures for specific tasks from the user's past business history. It can also analyze the user's business history and propose efficient handover procedures. This allows the system to provide the optimal handover procedure based on the user's business history.

[0109] The business handover support system can also include a feedback collection unit. This unit collects feedback from the successor and uses it to improve the manual. For example, the feedback collection unit allows the successor to evaluate the manual's content. It also allows the successor to suggest improvements to the manual. Furthermore, the feedback collection unit can automatically update the manual's content based on the successor's feedback. This allows for improvement of the manual's quality based on the successor's feedback.

[0110] The business handover support system can also include an emotion estimation unit. This unit estimates the user's emotions and adjusts the handover procedure based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit provides a concise and intuitive handover procedure. If the user is relaxed, it can also provide a handover procedure with detailed explanations. Furthermore, if the user is in a hurry, it can provide a short, to-the-point handover procedure. This allows the system to provide handover procedures tailored to the user's emotions.

[0111] The business handover support system can also be equipped with a business environment adaptation unit. This unit customizes the handover procedure based on the user's work environment. For example, if the user is working remotely, the unit provides a handover procedure suitable for a remote environment. It can also provide a handover procedure suitable for an office environment if the user is working in an office. Furthermore, if the user is performing on-site work, the unit can provide a handover procedure suitable for the on-site environment. This allows the system to provide handover procedures tailored to the user's specific work environment.

[0112] The business handover support system can also include a learning history analysis unit. This unit analyzes the user's learning history and proposes the optimal handover procedure. For example, it customizes the handover procedure based on the user's past learning. It can also provide detailed instructions for areas where the user's understanding is weak, based on their learning history. Furthermore, it can analyze the user's learning history and propose an efficient handover procedure. This allows the system to provide the optimal handover procedure based on the user's learning history.

[0113] The business handover support system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions and adjusts the manual's wording based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit creates a manual using concise and intuitive language. If the user is relaxed, it can also create a manual with detailed explanations and supplementary information. Furthermore, if the user is in a hurry, it can create a short, to-the-point manual. This allows the manual to be delivered in a way that aligns with the user's emotions.

[0114] The business handover support system can also be equipped with a business step prediction unit. This unit predicts the next business step to be performed based on the user's business history. For example, it analyzes patterns of past business steps performed by the user and proposes the next step. Furthermore, it can prioritize and suggest frequently performed steps based on the user's business history. Additionally, it can analyze the user's business history and propose efficient business steps. This allows the system to provide the optimal business steps based on the user's business history.

[0115] The business handover support system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions and adjusts the QA response's expression based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit provides a concise and intuitive response. If the user is relaxed, it can also provide a response with detailed explanations. Furthermore, if the user is in a hurry, it can provide a short, to-the-point response. This allows for QA responses to be delivered in an expression tailored to the user's emotions.

[0116] The business handover support system can also be equipped with an automated business step completion function. This function automatically completes business steps based on the business steps entered by the user, referencing manuals for similar past tasks. For example, when the user enters "data extraction," the automated business step completion function will automatically complete the "data extraction procedure" from manuals for similar past tasks. It can also automatically complete the report creation procedure when the user enters "report creation." Furthermore, it can automatically complete the meeting preparation procedure when the user enters "meeting preparation." This reduces the effort required for data entry by referencing manuals for similar past tasks.

[0117] The business handover support system can also include an emotion estimation unit. This unit estimates the user's emotions and adjusts the length of the manual based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit creates a short, concise manual. If the user is relaxed, it can create a longer manual with more detailed explanations. Furthermore, if the user is in a hurry, it can create a concise, quickly understandable manual. This allows the system to provide manuals of appropriate length based on the user's emotions.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The manual creation section generates the manual by inputting the business steps. For example, the manual is automatically generated when the user inputs each step of the business. Specifically, the user first inputs "Extract data," then "Summarize in Excel." In this way, the manual is completed by inputting each step of the business in order. Step 2: The comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding. For example, if the successor is asked, "Do you know about this operation?" and answers, "Yes, I do," the detailed explanation for that section is omitted. On the other hand, if the successor is asked, "Do you have experience with this task?" and answers, "No, I don't," the detailed explanation for that section is added. In this way, a manual tailored to the successor's knowledge level is provided. Step 3: The QA support unit provides QA support based on a manual customized by the comprehension assessment unit. For example, if a successor asks, "I don't understand how to do this," the AI ​​automatically provides an answer. This reduces inquiries to the predecessor and improves work efficiency.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the manual creation unit, comprehension assessment unit, and QA response unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the manual creation unit is implemented by the control unit 46A of the smart device 14, which automatically generates a manual when the user inputs each step of the work. The comprehension assessment unit is implemented by the specific processing unit 290 of the data processing unit 12, which customizes the manual according to the successor's level of understanding. The QA response unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically provides answers using AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the manual creation unit, comprehension assessment unit, and QA response unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the manual creation unit is implemented by the control unit 46A of the smart glasses 214, which automatically generates a manual when the user inputs each step of the work. The comprehension assessment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which customizes the manual according to the successor's level of understanding. The QA response unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which automatically provides answers using AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the manual creation unit, comprehension assessment unit, and QA response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the manual creation unit is implemented by the control unit 46A of the headset terminal 314, which automatically generates a manual when the user inputs each step of the work. The comprehension assessment unit is implemented by the specific processing unit 290 of the data processing unit 12, which customizes the manual according to the successor's level of understanding. The QA response unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically provides answers using AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the manual creation unit, comprehension assessment unit, and QA response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the manual creation unit is implemented by the control unit 46A of the robot 414, which automatically generates a manual when the user inputs each step of the work. The comprehension assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which customizes the manual according to the successor's level of understanding. The QA response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically provides answers using AI. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A manual creation unit that generates manuals by inputting business steps, A comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding, A QA response unit that performs QA response based on a manual customized by the aforementioned understanding level determination unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a business step input section for entering business steps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned comprehension level determination unit, It includes a comprehension questioning unit that asks questions to assess the successor's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned QA support unit is It includes an answer generation unit that automatically generates answers to questions from successors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The manual creation unit described above, Manuals are automatically generated by entering the business steps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned comprehension level determination unit, Customize the manual content according to the successor's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 7) The manual creation unit described above, The system estimates the user's emotions and adjusts the wording of the manual based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The manual creation unit described above, When entering business steps, the system automatically completes the input by referring to manuals for similar past tasks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The manual creation unit described above, When entering business steps, adjust the level of detail in the manual based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 10) The manual creation unit described above, The system estimates the user's emotions and adjusts the length of the manual based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The manual creation unit described above, When users input business steps, the system refers to their business history to suggest the most suitable steps. The system described in Appendix 1, characterized by the features described herein. (Note 12) The manual creation unit described above, When entering business steps, the manual content is customized based on the user's work environment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned comprehension level determination unit, The system estimates the user's emotions and adjusts the comprehension assessment questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned comprehension level determination unit, When assessing comprehension, the difficulty level of the questions is adjusted by referring to past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned comprehension level determination unit, When assessing comprehension, the questions are customized based on the user's work experience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned comprehension level determination unit, The system estimates the user's emotions and adjusts the order of questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned comprehension level determination unit, When assessing comprehension, the system selects the most appropriate questions by referring to the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned comprehension level determination unit, When assessing comprehension, the content of the questions is customized based on the user's work environment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned QA support unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned QA support unit is When handling QA, we refer to past question history to improve the accuracy of our answers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned QA support unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned QA support unit is When handling QA, prioritize answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned QA support unit is When handling Q&A, adjust the order of answers based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned business step input unit is: It estimates the user's emotions and adjusts the input method for business steps based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned business step input unit is: When entering data into a business process, the system will refer to past input history to suggest the most suitable input method. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned business step input unit is: When entering business steps, adjust the level of detail based on the importance of the task. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned business step input unit is: It estimates the user's emotions and determines the priority of the input steps based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned business step input unit is: When users input business steps, the system refers to their business history to suggest the most suitable steps. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned business step input unit is: When entering business steps, the input content is customized based on the user's work environment. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned comprehension question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned comprehension question section is, When asking comprehension questions, the difficulty level of the questions is adjusted by referring to past answer history. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned comprehension question section is, The system estimates the user's emotions and adjusts the order of questions based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned comprehension question section is, When asking comprehension questions, the system selects the most appropriate questions by referring to the user's learning history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned response generation unit, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned response generation unit, When generating answers, we refer to past question history to improve the accuracy of the answers. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned response generation unit, It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned response generation unit, When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0192] 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. A manual creation unit that generates manuals by inputting business steps, A comprehension assessment unit customizes the manual generated by the manual creation unit to match the successor's level of understanding, A QA response unit that performs QA response based on a manual customized by the aforementioned understanding level determination unit, Equipped with A system characterized by the following features.

2. It is equipped with a business step input section for entering business steps. The system according to feature 1.

3. The aforementioned comprehension level determination unit, It includes a comprehension questioning unit that asks questions to assess the successor's level of understanding. The system according to feature 1.

4. The aforementioned QA support unit is It includes an answer generation unit that automatically generates answers to questions from successors. The system according to feature 1.

5. The manual creation unit described above, Manuals are automatically generated by entering the business steps. The system according to feature 1.

6. The aforementioned comprehension level determination unit, Customize the manual content according to the successor's level of understanding. The system according to feature 1.

7. The manual creation unit described above, The system estimates the user's emotions and adjusts the wording of the manual based on those estimated emotions. The system according to feature 1.

8. The manual creation unit described above, When entering business steps, the system automatically completes the input by referring to manuals for similar past tasks. The system according to feature 1.

9. The manual creation unit described above, When entering business steps, adjust the level of detail in the manual based on the importance of the task. The system according to feature 1.

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