system

Abstract

We provide the system. [Solution] Means for recording standard work instructions, A means of receiving inquiries from users, An artificial intelligence means for evaluating the user's level of understanding based on the aforementioned inquiry, A means of customizing and providing the standard work instructions based on the aforementioned evaluation, A system that includes this.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to create appropriate content for work instruction manuals due to differences in the understanding levels and knowledge amounts of the creators and users. Since conventional instruction manuals are provided with the same content for all users, they are not optimized for individual understanding levels and are factors that reduce the efficiency of work acquisition. Considering such differences, it is necessary to customize instruction manuals according to the knowledge levels of each user.

Means for Solving the Problems

[0005] This invention provides a system that creates standard work instructions and receives inquiries from users based on those instructions. Using artificial intelligence technology, the system can evaluate the user's knowledge level from their inquiries and, based on that evaluation, reorganize the standard instructions in a way that is optimal for the user. This makes it possible to provide customized instructions tailored to each user's level of understanding, thereby improving the efficiency of job acquisition.

[0006] A "standard work instruction" is a document that describes a specific business process and includes a set of operating procedures that a typical user should follow.

[0007] "User inquiries" refer to the act of submitting questions to the system regarding parts of the instructions that the user did not understand or wanted more details about.

[0008] "Artificial intelligence tools" refer to algorithms and programs that analyze user inquiries and automatically evaluate the user's knowledge level and understanding based on that content.

[0009] "Methods of providing customized instructions" refers to the process of modifying the content of standard operating instructions to match the user's knowledge level and providing them to the user as optimized instructions.

[0010] "Means for analyzing term frequency and complexity" refers to methods for analyzing the frequency of use of technical terms and the complexity of sentences included in user inquiries, and for evaluating the user's knowledge level.

[0011] "Pre-processing for temporary storage and provision to the user" refers to processes that include storing customized instructions in a database for a short period of time and then transferring them to the user. [Brief explanation of the drawing]

[0012] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for customizing standard work instructions to suit each user's level of understanding. The embodiments thereof are described in detail below.

[0034] The system begins with a user (manual creator) creating standard work instructions related to their tasks and uploading them to the server via a terminal. The server then stores these instructions in a database, making them accessible to all users.

[0035] Users (manual users) access and learn from this standard operating instructions. If they have any questions or need further details, they enter their inquiries via their terminal and send them to the server. Based on the received inquiries, the server uses artificial intelligence to assess the user's knowledge level.

[0036] Artificial intelligence analyzes the frequency and complexity of the terminology used by the user to determine the user's level of understanding. Once the evaluation is complete, the server customizes the standard operating instructions based on the information provided by the AI. This customization may involve reorganizing the instructions to include simpler explanations, additional examples, and diagrams.

[0037] Once customized, the instruction manual is temporarily stored on the server and provided to the user via the terminal. This allows users to efficiently learn using instruction manuals optimized for their individual level and acquire the skills necessary for their work.

[0038] As a concrete example, when a user learns how to use a new tool for a particular task, they refer to the standard instruction manual, and if they have an insufficient understanding of a specific function, they post a question about that part. The server analyzes the question and determines that the user understands the basic operations but needs further information on advanced settings. Based on this determination, the server generates a customized manual that includes detailed procedures for advanced settings, along with concise and easy-to-understand explanations, and provides it to the user.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user (manual creator) creates a work instruction sheet and uploads it to the server via their terminal. The server then stores the received data in a database.

[0042] Step 2:

[0043] Users (manual users) access saved standard operating instructions and learn from their contents. They can input questions via their terminal about areas where their understanding is lacking or where they want more detailed information, and send these questions to the server.

[0044] Step 3:

[0045] The server receives questions from users and sends their content to an artificial intelligence model. The AI ​​analyzes the questions and evaluates the user's knowledge level and level of understanding.

[0046] Step 4:

[0047] The generating AI determines the user's level of understanding based on the frequency and complexity of terms in the question. Based on the determination, it provides the server with information on how detailed the information needs to be.

[0048] Step 5:

[0049] The server customizes the standard work instructions based on the judgment results provided by the artificial intelligence. The customized instructions include additional detailed explanations, specific examples, and diagrams.

[0050] Step 6:

[0051] The customized instructions are temporarily saved and then sent to the user. The user can receive this information on their device and continue learning.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] Existing standard instruction manuals are difficult to customize to suit each user's level of understanding, resulting in challenges for users to efficiently and effectively acquire the necessary skills. In particular, there is a lack of methods to individually improve instruction manuals based on user inquiries and feedback, thus maximizing the effectiveness of learning.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for recording instruction materials, means for receiving inquiries from users, artificial intelligence means for evaluating the user's level of understanding from the inquiries, means for improving and providing the instruction materials based on the evaluation, and means for temporarily storing the improved instruction materials and performing preprocessing before providing them to the user. This makes it possible to provide individually optimized instruction materials according to each user's level of understanding, thereby supporting efficient and effective learning.

[0057] "Instruction materials" are documents that describe work procedures and operating methods, and represent the standard workflow for a given task.

[0058] "User" refers to an individual who uses instruction materials to perform tasks, or an individual who creates instruction materials to provide information.

[0059] "Means of receiving inquiries" refers to the method by which users can send questions or points of confusion regarding instruction materials to the server and receive them.

[0060] "An artificial intelligence method for evaluating comprehension" refers to artificial intelligence technology used to analyze the content of user inquiries and determine the user's knowledge level and level of comprehension based on that content.

[0061] "Methods of providing improved information" refers to a method of adjusting instructional materials to the user's level based on their level of understanding, as assessed by artificial intelligence, and providing optimized information.

[0062] "Means for temporarily storing and pre-processing information" refers to methods for temporarily storing improved instructional materials and presenting them to the user in the appropriate format when needed.

[0063] A "generative AI model" is an artificial intelligence model used to analyze a user's level of understanding, and refers to a technology that deeply understands and evaluates the content of a question.

[0064] This invention is a system that customizes instruction materials according to each user's level of understanding. Specifically, it begins with creating instruction materials using a terminal and uploading them to a server. The hardware used includes personal computers and tablet devices operated by the user, and the software includes document creation tools and web browsers. On the server side, a database management system and artificial intelligence models, particularly generative AI models, are provided. This allows the server to store instruction materials in a database and facilitate smooth communication with users.

[0065] When a user accesses instructional materials and progresses through their learning, they can submit inquiries through their device if they have questions or want more details. These inquiries are sent via natural language input and received by the server. The server utilizes a generative AI model to analyze the received inquiries. This model evaluates the frequency and complexity of the terms used by the user and determines their level of understanding. Prompts such as "Analyze this user's question and evaluate their level of understanding" are used.

[0066] Based on the assessed level of understanding, the server customizes the instruction materials. This includes using clearer language and adding diagrams and examples as needed. Furthermore, for areas requiring specialized knowledge, it can add more detailed steps. For example, when learning how to use a new software tool, instructions are generated that cover everything from basic to advanced settings, tailored to the user's skill level.

[0067] The generated customized instructions are temporarily stored on the server and provided to the user via their terminal. This allows users to efficiently proceed with learning and practical work using materials optimized to their level of understanding.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] Users create instruction documents related to their work and upload them to the server using their terminal. The input is the instruction document file created by the user (e.g., PDF, Word format). The server stores the received documents in a database and records the relevant metadata. As output, an access link to the instruction document is generated.

[0071] Step 2:

[0072] The user accesses the server through their device and views the stored instruction materials. The input is the user's access request, and the server processes the viewing request and provides the corresponding instruction materials. The output is the instruction materials displayed on the user's device.

[0073] Step 3:

[0074] The user refers to the instruction materials, enters inquiries about parts they are unsure of or would like more details about, and sends them to the server via their terminal. The input is the user's inquiry, and the output is the inquiry data received by the server.

[0075] Step 4:

[0076] The server analyzes the received query and uses a generative AI model to evaluate the user's understanding. The input is user query data, and the server performs data calculations by analyzing the frequency and complexity of terms. The output is the user understanding evaluation result.

[0077] Step 5:

[0078] The server customizes the instruction materials based on the comprehension assessment results. The input consists of the user's comprehension assessment results and the original instruction materials. With the help of a generative AI model, the server processes the data by adjusting wording and content, and adding necessary diagrams and examples. The output is the customized instruction materials.

[0079] Step 6:

[0080] The server temporarily stores customized instruction materials and provides them to the user via the terminal. The input is the customized instruction materials, and the output is a download or display link provided to the user's terminal. This allows the user to access materials appropriate to their level and learn efficiently.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] While factories require workers with varying skill levels to perform tasks efficiently, current work instruction information is insufficiently customized to each worker's level of understanding, resulting in ineffective guidance for diverse workforces. Furthermore, the limited methods for obtaining necessary information on the spot during work raise concerns about decreased work efficiency.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes a device for recording standard work instruction information, a device for receiving inquiries from users, and a knowledge processing device for evaluating the user's level of understanding from the inquiries. This makes it possible to provide work instruction information adapted to each worker's level of understanding in real time via a visual display device.

[0086] "Standard work instructions" are documents that describe the procedures and methods that workers should follow in workplaces such as factories, and are used to ensure the efficiency and safety of each task.

[0087] "User" refers to an individual or group responsible for referring to standard work instruction information and performing work based on it.

[0088] An "inquiry" refers to a question or request that a user submits regarding work instruction information if they have a misunderstanding or require additional explanation.

[0089] A "knowledge processing device" is a device that uses artificial intelligence technology to analyze user inquiries and evaluate the user's level of understanding.

[0090] A "visual display device" is a device that visualizes information to the user in real time via devices such as smart glasses.

[0091] "Real-time delivery" means providing necessary information with minimal interruption to work by instantly displaying customized work instructions in response to user inquiries.

[0092] This invention is a system that customizes standard work instruction information according to each worker's level of understanding and provides it in real time, enabling workers in a factory to perform their tasks efficiently. The main components of the system include a server, a knowledge processing device, and a visual display device. Its detailed operation and usage are described below.

[0093] First, the server records standard work instruction information in a digital format and stores it in a database. This database must be accessible to all workers in the factory. When a question arises, users send the content to the server via an input device. This input device may include devices with built-in voice recognition capabilities.

[0094] The server receives this query and uses a knowledge processing unit to analyze the data. The knowledge processing unit leverages a generative AI model to evaluate the user's understanding based on the frequency and complexity of the terms included in the query. This process utilizes machine learning libraries such as TENSORFLOW®.

[0095] Based on the evaluation results, the server generates customized standard work instruction information. This customized information is provided to the user in real time through a visual display device. Smart glasses or head-mounted displays are used as visual display devices.

[0096] For example, when setting up a new machine, if an operator verbally inputs "I don't know how to operate this function," the system converts that information into text and analyzes it using a knowledge processing device. As a result, detailed operating procedures and video manuals tailored to the operator's level of understanding are customized and displayed on the smart glasses' screen.

[0097] An example of a prompt message for a generated AI model is, "Please customize and provide details regarding the setup procedure for the new machine."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The user accesses standard work instruction information through a terminal. The input here is the type of work instruction information the user requires, and the output is digitized standard work instruction information. The terminal retrieves and displays the information from the server.

[0101] Step 2:

[0102] Users input inquiries into the terminal in voice or text format regarding parts they find difficult to understand or where additional explanations are needed. These inquiries constitute the input data and are sent to the server. The server then forwards this data to the knowledge processing unit.

[0103] Step 3:

[0104] The server analyzes the query using a knowledge processing device. Here, the input data is the query content, and the output is the user's comprehension evaluation result. The knowledge processing device analyzes the frequency and complexity of terminology through a generative AI model and evaluates the user's comprehension level.

[0105] Step 4:

[0106] The server customizes standard work instruction information based on the evaluation results. The input here is the comprehension evaluation result, and the output is the customized work instruction information. The server adds explanations and details that match the evaluation and restructures the information.

[0107] Step 5:

[0108] The server sends customized work instruction information to a visual display device. The input is customized information, and the output is information displayed in real time on the user's visual display device. The terminal displays this information, allowing the user to continue working.

[0109] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0110] This invention combines a system that customizes work instructions for each user with an emotion engine that recognizes the user's emotions. The aim of this system is to comprehensively evaluate the user's level of understanding and emotions and provide instructions optimized for the user.

[0111] The system first allows the user (manual creator) to create a standard work instruction document and upload it to the server via a terminal. The server then stores this instruction document in a database, making it accessible to the user (manual user).

[0112] The user refers to the instructions, and if there are any unclear points, they input a question through their terminal and send it to the server. The server analyzes the user's question using a generating AI to determine the user's knowledge level.

[0113] In addition, the emotion engine recognizes the user's emotional state through their words and actions. This information is analyzed from, for example, the phrasing of text, the content of repeated questions, and the typing speed.

[0114] After assessing the user's knowledge level and emotional state, the server uses this data to customize the instructions in a way that best suits the user. Specifically, it may add explanations of technical terms or include more easily understandable examples. Furthermore, if the user is likely to be under stress, the server may add kind words and encouraging messages to the instructions, helping to reduce the user's work pressure.

[0115] Once customized, the instructions are temporarily stored on the server before being sent to the user's terminal. This allows users to refer to instructions tailored to their level of understanding and preferences, enabling them to learn and perform tasks more efficiently.

[0116] For example, if a user is learning how to use new software and the operating procedures are complex and stressful, the system will simplify the instructions and add explanations in user-friendly language. Providing information tailored to the user's situation can improve the efficiency of learning new skills.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The user (manual creator) uses a terminal to create a standard work instruction sheet and uploads it to the server. The server then stores it in its database.

[0120] Step 2:

[0121] The user (manual user) accesses the database via a terminal and views the standard operating instructions. If there are any parts that are difficult to understand while viewing, they can input and send questions from the terminal.

[0122] Step 3:

[0123] The server receives questions from users and sends that data to a generating AI. The generating AI analyzes the content of the questions and evaluates the user's knowledge level.

[0124] Step 4:

[0125] An emotion engine running on the device analyzes the user's input speed, keyboard operation, and expressions to recognize the user's emotional state.

[0126] Step 5:

[0127] The server customizes standard work instructions for the user based on evaluation results from the generating AI and emotional states from the emotion engine. For example, it adds detailed explanations to complex operations and inserts relaxing language if the user is feeling stressed.

[0128] Step 6:

[0129] The server temporarily stores the customized instructions in storage and sends them to the terminal. The terminal displays the instructions to the user, who then continues their work while referring to them.

[0130] Step 7:

[0131] The goal is to ensure that users receive updated instructions and can proceed with their work with confidence, based on information that aligns with their understanding and emotional needs.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] Conventional instruction management systems often fail to provide instructions in a format that is easy for users to understand and does not take into account their emotional state, which can hinder efficient work execution and learning. This can lead to increased user stress and a decline in the quality of work and learning speed.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes means for recording standard instructions, means for receiving inquiries from users, and artificial intelligence means for evaluating the user's knowledge level and emotional state. This makes it possible to provide instructions customized according to the user's level of understanding and emotional state.

[0137] A "standard instruction manual" is a document that describes the operating procedures or steps that are generally adopted for a particular task or operation.

[0138] "User" refers to a person who operates the system to create or refer to instruction manuals.

[0139] An "inquiry" refers to information that users use to communicate unclear points or questions that arise when referring to instructions to the server.

[0140] "An artificial intelligence method for evaluating knowledge levels" refers to a technology that analyzes the content of inquiries received from users and measures the specific knowledge and level of understanding that users possess.

[0141] A "means for evaluating emotional state" refers to a technology that analyzes a user's input and actions to determine and evaluate their emotional state.

[0142] "Means of providing customized solutions" refers to the techniques and processes for appropriately adjusting the content of standard instructions based on the user's knowledge level and emotional state, and providing them in the most optimal form for the user.

[0143] This invention is a system for providing standard instructions in a way that is easy for users to understand and that takes their emotional state into consideration. The system is broadly composed of a server, a terminal, and the user.

[0144] The server receives standard instruction manuals from users (instruction manual creators) via their terminals and stores them in appropriate databases. During this process, data management software such as MySQL® or MongoDB is used to organize and store metadata. Users (instruction manual users) can access these databases and retrieve the necessary instruction manuals.

[0145] If a user has a question while referring to the instructions, they can input the question through an interface on their terminal and send it to the server. The server receives this inquiry and analyzes the user's knowledge level using a generative AI model. This process utilizes natural language processing tools and models, such as OpenAI® language models.

[0146] Furthermore, the emotion engine evaluates the user's emotional state based on their input. This evaluation uses text analysis technology to infer emotions from factors such as input speed and phrasing patterns. Based on this information, the server appropriately customizes the standard instructions. Specific examples include adding explanations of technical terms and using examples in more user-friendly language. In addition, if the system determines that the user is experiencing high stress levels, it may add encouraging messages.

[0147] Finally, the customized instructions are temporarily stored on the server's storage and sent to the user's terminal. At this point, the terminal displays the instructions on the screen in an appropriate format. The system also uses prompts to optimize the instructions according to the user's state. For example, a prompt might read: "Please provide clearer explanations and simpler examples for the operating procedures that the user finds difficult. Also, please add words of encouragement, as the user may be feeling stressed."

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The user creates a standard instruction manual and uploads the standard instruction manual file to the server via a terminal. The input is a digital file of the instruction manual. Specifically, the user selects the file through a dedicated interface and presses the send button. The output is a standard instruction manual file saved on the server.

[0151] Step 2:

[0152] The server stores the received standard instructions in a database. The input is a digital file uploaded by the user, and metadata is automatically generated and associated processing is performed when storing it in the database. Specifically, information including the file name, creation date, and creator is tagged. The output is the standard instruction data recorded in the database.

[0153] Step 3:

[0154] If a user has difficulty understanding the instructions, they can enter a question via their terminal and send it to the server. The input is a free-form question text entered by the user. Specifically, the user enters the question into the text box on the terminal and presses the send button. The output at this point is the question text that is sent to the server.

[0155] Step 4:

[0156] The server analyzes received questions using a generative AI model. The input is the user's question text, which is tokenized to extract important keywords. Subsequently, the server evaluates the user's knowledge level based on this. Specifically, the analysis results are scored to determine how specialized the knowledge is. The output is evaluation data indicating the user's knowledge level.

[0157] Step 5:

[0158] The server uses an emotion engine to evaluate the user's emotional state. Inputs include the user's question text and input speed, and the server determines emotions through text analysis. Specifically, it applies algorithms to measure positive and negative emotions and evaluates multiple emotions as needed. The output is data that explicitly indicates the user's emotional state.

[0159] Step 6:

[0160] The server customizes the standard instructions based on the knowledge level and emotional state assessment obtained in the previous step. The inputs are the assessment data and the original standard instructions. Specifically, the server inserts additional explanations and examples into the instructions and modifies the text tone to suit the user. The output is the customized instructions.

[0161] Step 7:

[0162] The server temporarily stores the customized instructions in storage and then sends them to the user's terminal. The input is the customized instruction data, and the output is the instructions displayed on the user's terminal. Specifically, the server verifies the integrity of the data before sending it to the terminal using a communication protocol.

[0163] (Application Example 2)

[0164] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0165] The challenge lies in reducing the stress users experience when using work instructions, particularly those related to understanding and emotions, and thereby improving work efficiency. In particular, in factory and other on-site work environments, there is a lack of instructions tailored to the workers' level of understanding, as well as insufficient emotional support.

[0166] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0167] In this invention, the server includes a device for recording standard work instructions, a device for receiving inquiries from users, an artificial intelligence device for evaluating the user's level of understanding, an emotion recognition device for recognizing and evaluating the user's emotional state, and a device for customizing and providing instructions based on the level of understanding and emotional state. This makes it possible to provide instructions optimized for the user's knowledge level and emotions, thereby reducing the burden on workers and improving work efficiency.

[0168] A "standard work instruction sheet" is a document that describes the procedures and precautions for performing a specific task, and is usually referred to when carrying out work.

[0169] A "device" is a collection of technical components designed to perform a specific function, and includes hardware and software for performing specific operations or processes.

[0170] An "inquiry" refers to a question or request made by a user to obtain unclear points or specific information, and is an action of seeking information from a system.

[0171] "Artificial intelligence" refers to the technology of computer systems that mimic human intellectual behavior and logical reasoning, and specifically to the ability to analyze data and make inferences and judgments.

[0172] "Emotion recognition" is a technology that analyzes and judges a user's emotional state, evaluating the user's mental state from factors such as voice, facial expressions, and behavior.

[0173] "Customization" refers to modifying content to suit the specific needs and circumstances of a particular user, thereby adjusting the content to make it easier for the user to understand and use.

[0174] The system implementing this invention supports users in efficiently utilizing work instructions. The system operates using a terminal that records standard work instructions and receives inquiries from users. The server performs artificial intelligence (AI) processing to analyze user inquiries and thereby evaluate the user's knowledge level. Generative AI models such as OpenAI are used for this purpose.

[0175] Furthermore, by using an emotion recognition device, the system determines the user's emotional state from their facial expressions and tone of voice. For this purpose, for example, Microsoft's Emotion API is utilized. Based on this evaluated information, the server customizes the work instructions in a way that is optimized for the user. This customization includes not only simplifying the terminology in the instructions but also adding encouraging and reassuring messages.

[0176] As a concrete example, consider a scenario where a user uses smart glasses to refer to work instructions. If the user makes a mistake during the task, the glasses detect it and send a query to the server. The server then assesses the user's understanding and emotional state and provides instructions that gently suggest specific next steps. This might be a message like, "You've got the first step down! Now let's try this."

[0177] Examples of prompt statements for a generative AI model are as follows:

[0178] User question: "I don't know where to install this part."

[0179] Prompt message: "The user has accessed the instruction manual page and asked a question about the installation location of a part. The user's knowledge level is intermediate, and they appear slightly stressed. Please provide clear and encouraging instructions."

[0180] This system allows users to receive appropriate guidance based on their level of understanding and emotional needs, enabling them to acquire job skills effectively and efficiently.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The user uses a terminal to refer to work instructions and enters any questions or points of confusion during the process. The entered inquiries are then sent from the terminal to the server.

[0184] Step 2:

[0185] The server analyzes the received inquiry and uses a generative AI model to determine the user's level of understanding. The user's question is used as input, and the user's knowledge level is output as the analysis result.

[0186] Step 3:

[0187] A separate device, an emotion recognition device, detects the user's facial expressions and voice data and analyzes their emotional state. Here, the user's real-time images and voice are used as input, and the user's emotional state is obtained as output.

[0188] Step 4:

[0189] The server integrates the results of the generated AI model and the emotion recognition results to customize the optimal instructions according to the user's level of understanding and emotional state. The input is the aforementioned analysis results, and the output is a customized set of instructions.

[0190] Step 5:

[0191] The customized instructions are temporarily stored on the server. They are then converted to the required format and sent to the terminal.

[0192] Step 6:

[0193] The terminal displays the received customization instructions to the user. The user can then proceed with the work based on the instructions. As a result, the user's work efficiency improves.

[0194] 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.

[0195] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0201] 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.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0203] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] 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.

[0205] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The 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.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] This invention is a system for customizing standard work instructions to suit each user's level of understanding. The embodiments thereof are described in detail below.

[0211] The system begins with a user (manual creator) creating standard work instructions related to their tasks and uploading them to the server via a terminal. The server then stores these instructions in a database, making them accessible to all users.

[0212] Users (manual users) access and learn from this standard operating instructions. If they have any questions or need further details, they enter their inquiries via their terminal and send them to the server. Based on the received inquiries, the server uses artificial intelligence to assess the user's knowledge level.

[0213] Artificial intelligence analyzes the frequency and complexity of the terminology used by the user to determine the user's level of understanding. Once the evaluation is complete, the server customizes the standard operating instructions based on the information provided by the AI. This customization may involve reorganizing the instructions to include simpler explanations, additional examples, and diagrams.

[0214] Once customized, the instruction manual is temporarily stored on the server and provided to the user via the terminal. This allows users to efficiently learn using instruction manuals optimized for their individual level and acquire the skills necessary for their work.

[0215] As a concrete example, when a user learns how to use a new tool for a particular task, they refer to the standard instruction manual, and if they have an insufficient understanding of a specific function, they post a question about that part. The server analyzes the question and determines that the user understands the basic operations but needs further information on advanced settings. Based on this determination, the server generates a customized manual that includes detailed procedures for advanced settings, along with concise and easy-to-understand explanations, and provides it to the user.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user (manual creator) creates a work instruction sheet and uploads it to the server via their terminal. The server then stores the received data in a database.

[0219] Step 2:

[0220] Users (manual users) access saved standard operating instructions and learn from their contents. They can input questions via their terminal about areas where their understanding is lacking or where they want more detailed information, and send these questions to the server.

[0221] Step 3:

[0222] The server receives questions from users and sends their content to an artificial intelligence model. The AI ​​analyzes the questions and evaluates the user's knowledge level and level of understanding.

[0223] Step 4:

[0224] The generating AI determines the user's level of understanding based on the frequency and complexity of terms in the question. Based on the determination, it provides the server with information on how detailed the information needs to be.

[0225] Step 5:

[0226] The server customizes the standard work instructions based on the judgment results provided by the artificial intelligence. The customized instructions include additional detailed explanations, specific examples, and diagrams.

[0227] Step 6:

[0228] The customized instructions are temporarily saved and then sent to the user. The user can receive this information on their device and continue learning.

[0229] (Example 1)

[0230] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0231] Existing standard instruction manuals are difficult to customize to suit each user's level of understanding, resulting in challenges for users to efficiently and effectively acquire the necessary skills. In particular, there is a lack of methods to individually improve instruction manuals based on user inquiries and feedback, thus maximizing the effectiveness of learning.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] In this invention, the server includes means for recording instruction materials, means for receiving inquiries from users, artificial intelligence means for evaluating the user's level of understanding from the inquiries, means for improving and providing the instruction materials based on the evaluation, and means for temporarily storing the improved instruction materials and performing preprocessing before providing them to the user. This makes it possible to provide individually optimized instruction materials according to each user's level of understanding, thereby supporting efficient and effective learning.

[0234] "Instruction materials" are documents that describe work procedures and operating methods, and represent the standard workflow for a given task.

[0235] "User" refers to an individual who uses instruction materials to perform tasks, or an individual who creates instruction materials to provide information.

[0236] "Means of receiving inquiries" refers to the method by which users can send questions or points of confusion regarding instruction materials to the server and receive them.

[0237] "An artificial intelligence method for evaluating comprehension" refers to artificial intelligence technology used to analyze the content of user inquiries and determine the user's knowledge level and level of comprehension based on that content.

[0238] "Methods of providing improved information" refers to a method of adjusting instructional materials to the user's level based on their level of understanding, as assessed by artificial intelligence, and providing optimized information.

[0239] "Means for temporarily storing and pre-processing information" refers to methods for temporarily storing improved instructional materials and presenting them to the user in the appropriate format when needed.

[0240] A "generative AI model" is an artificial intelligence model used to analyze a user's level of understanding, and refers to a technology that deeply understands and evaluates the content of a question.

[0241] This invention is a system that customizes instruction materials according to each user's level of understanding. Specifically, it begins with creating instruction materials using a terminal and uploading them to a server. The hardware used includes personal computers and tablet devices operated by the user, and the software includes document creation tools and web browsers. On the server side, a database management system and artificial intelligence models, particularly generative AI models, are provided. This allows the server to store instruction materials in a database and facilitate smooth communication with users.

[0242] When a user accesses instructional materials and progresses through their learning, they can submit inquiries through their device if they have questions or want more details. These inquiries are sent via natural language input and received by the server. The server utilizes a generative AI model to analyze the received inquiries. This model evaluates the frequency and complexity of the terms used by the user and determines their level of understanding. Prompts such as "Analyze this user's question and evaluate their level of understanding" are used.

[0243] Based on the assessed level of understanding, the server customizes the instruction materials. This includes using clearer language and adding diagrams and examples as needed. Furthermore, for areas requiring specialized knowledge, it can add more detailed steps. For example, when learning how to use a new software tool, instructions are generated that cover everything from basic to advanced settings, tailored to the user's skill level.

[0244] The generated customized instructions are temporarily stored on the server and provided to the user via their terminal. This allows users to efficiently proceed with learning and practical work using materials optimized to their level of understanding.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] Users create instruction documents related to their work and upload them to the server using their terminal. The input is the instruction document file created by the user (e.g., PDF, Word format). The server stores the received documents in a database and records the relevant metadata. As output, an access link to the instruction document is generated.

[0248] Step 2:

[0249] The user accesses the server through their device and views the stored instruction materials. The input is the user's access request, and the server processes the viewing request and provides the corresponding instruction materials. The output is the instruction materials displayed on the user's device.

[0250] Step 3:

[0251] The user refers to the instruction materials, enters inquiries about parts they are unsure of or would like more details about, and sends them to the server via their terminal. The input is the user's inquiry, and the output is the inquiry data received by the server.

[0252] Step 4:

[0253] The server analyzes the received query and uses a generative AI model to evaluate the user's understanding. The input is user query data, and the server performs data calculations by analyzing the frequency and complexity of terms. The output is the user understanding evaluation result.

[0254] Step 5:

[0255] The server customizes the instruction materials based on the comprehension assessment results. The input consists of the user's comprehension assessment results and the original instruction materials. With the help of a generative AI model, the server processes the data by adjusting wording and content, and adding necessary diagrams and examples. The output is the customized instruction materials.

[0256] Step 6:

[0257] The server temporarily stores customized instruction materials and provides them to the user via the terminal. The input is the customized instruction materials, and the output is a download or display link provided to the user's terminal. This allows the user to access materials appropriate to their level and learn efficiently.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] While factories require workers with varying skill levels to perform tasks efficiently, current work instruction information is insufficiently customized to each worker's level of understanding, resulting in ineffective guidance for diverse workforces. Furthermore, the limited methods for obtaining necessary information on the spot during work raise concerns about decreased work efficiency.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes a device for recording standard work instruction information, a device for receiving inquiries from users, and a knowledge processing device for evaluating the user's level of understanding from the inquiries. This makes it possible to provide work instruction information adapted to each worker's level of understanding in real time via a visual display device.

[0263] "Standard work instructions" are documents that describe the procedures and methods that workers should follow in workplaces such as factories, and are used to ensure the efficiency and safety of each task.

[0264] "User" refers to an individual or group responsible for referring to standard work instruction information and performing work based on it.

[0265] An "inquiry" refers to a question or request that a user submits regarding work instruction information if they have a misunderstanding or require additional explanation.

[0266] A "knowledge processing device" is a device that uses artificial intelligence technology to analyze user inquiries and evaluate the user's level of understanding.

[0267] A "visual display device" is a device that visualizes information to the user in real time via devices such as smart glasses.

[0268] "Real-time delivery" means providing necessary information with minimal interruption to work by instantly displaying customized work instructions in response to user inquiries.

[0269] This invention is a system that customizes standard work instruction information according to each worker's level of understanding and provides it in real time, enabling workers in a factory to perform their tasks efficiently. The main components of the system include a server, a knowledge processing device, and a visual display device. Its detailed operation and usage are described below.

[0270] First, the server records standard work instruction information in a digital format and stores it in a database. This database must be accessible to all workers in the factory. When a question arises, users send the content to the server via an input device. This input device may include devices with built-in voice recognition capabilities.

[0271] The server receives this query and uses a knowledge processing unit to analyze the data. The knowledge processing unit leverages a generative AI model to evaluate the user's understanding based on the frequency and complexity of the terms included in the query. This process utilizes machine learning libraries such as TensorFlow.

[0272] Based on the evaluation results, the server generates customized standard work instruction information. This customized information is provided to the user in real time through a visual display device. Smart glasses or head-mounted displays are used as visual display devices.

[0273] For example, when setting up a new machine, if an operator verbally inputs "I don't know how to operate this function," the system converts that information into text and analyzes it using a knowledge processing device. As a result, detailed operating procedures and video manuals tailored to the operator's level of understanding are customized and displayed on the smart glasses' screen.

[0274] An example of a prompt message for a generated AI model is, "Please customize and provide details regarding the setup procedure for the new machine."

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The user accesses standard work instruction information through a terminal. The input here is the type of work instruction information the user requires, and the output is digitized standard work instruction information. The terminal retrieves and displays the information from the server.

[0278] Step 2:

[0279] The user inputs an inquiry to the terminal in voice or text form regarding difficult-to-understand parts or points that require additional explanation. This inquiry is the input data and is sent to the server. The server transfers this to the knowledge processing device.

[0280] Step 3:

[0281] The server analyzes the inquiry using the knowledge processing device. Here, the input data is the inquiry content, and the output is the user's comprehension evaluation result. The knowledge processing device analyzes the usage frequency and complexity of terms through the generated AI model and evaluates the user's comprehension.

[0282] Step 4:

[0283] The server customizes the standard work instruction information based on the evaluation result. Here, the input is the comprehension evaluation result, and the output is the customized work instruction information. The server adds explanations and detailed information that match the evaluation and reorganizes the information.

[0284] Step 5:

[0285] The server sends the customized work instruction information to the visual display device. The input is the customized information, and the output is the information that is displayed in real time on the user's visual display device. The terminal displays this information so that the user can continue with the work.

[0286] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0287] <x This invention combines a system that customizes work instructions for each user with an emotion engine that recognizes the user's emotions. The aim of this system is to comprehensively evaluate the user's level of understanding and emotions and provide instructions optimized for the user.

[0288] The system first allows the user (manual creator) to create a standard work instruction document and upload it to the server via a terminal. The server then stores this instruction document in a database, making it accessible to the user (manual user).

[0289] The user refers to the instructions, and if there are any unclear points, they input a question through their terminal and send it to the server. The server analyzes the user's question using a generating AI to determine the user's knowledge level.

[0290] In addition, the emotion engine recognizes the user's emotional state through their words and actions. This information is analyzed from, for example, the phrasing of text, the content of repeated questions, and the typing speed.

[0291] After assessing the user's knowledge level and emotional state, the server uses this data to customize the instructions in a way that best suits the user. Specifically, it may add explanations of technical terms or include more easily understandable examples. Furthermore, if the user is likely to be under stress, the server may add kind words and encouraging messages to the instructions, helping to reduce the user's work pressure.

[0292] Once customized, the instructions are temporarily stored on the server before being sent to the user's terminal. This allows users to refer to instructions tailored to their level of understanding and preferences, enabling them to learn and perform tasks more efficiently.

[0293] For example, if a user is learning how to use new software and the operating procedures are complex and stressful, the system will simplify the instructions and add explanations in user-friendly language. Providing information tailored to the user's situation can improve the efficiency of learning new skills.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The user (manual creator) uses a terminal to create a standard work instruction sheet and uploads it to the server. The server then stores it in its database.

[0297] Step 2:

[0298] The user (manual user) accesses the database via a terminal and views the standard operating instructions. If there are any parts that are difficult to understand while viewing, they can input and send questions from the terminal.

[0299] Step 3:

[0300] The server receives questions from users and sends that data to a generating AI. The generating AI analyzes the content of the questions and evaluates the user's knowledge level.

[0301] Step 4:

[0302] An emotion engine running on the device analyzes the user's input speed, keyboard operation, and expressions to recognize the user's emotional state.

[0303] Step 5:

[0304] The server customizes standard work instructions for the user based on evaluation results from the generating AI and emotional states from the emotion engine. For example, it adds detailed explanations to complex operations and inserts relaxing language if the user is feeling stressed.

[0305] Step 6:

[0306] The server temporarily stores the customized instruction manual in the storage and sends the instruction manual to the terminal. The terminal displays the instruction manual to the user, and the user continues the operation while viewing it.

[0307] Step 7:

[0308] The user receives the updated instruction manual and is enabled to proceed with the operation with confidence based on the information that matches their understanding level and sentiment.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In the conventional instruction manual management system, it is not necessarily in an easy-to-understand form for the user and does not consider the user's emotional state, which may hinder the efficient execution of operations and learning. As a result, there are problems such as an increase in the user's stress and a decrease in the quality and acquisition speed of operations.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0313] In this invention, the server includes means for recording a standard instruction manual, means for receiving inquiries from users, and an artificial intelligence means for evaluating the user's knowledge level and emotional state. This enables the provision of an instruction manual customized according to the user's understanding level and emotional state.

[0314] The "standard instruction manual" is a document that describes the operation procedures or processes generally adopted in specific operations or tasks.

[0315] "User" refers to a person who operates the system to create or refer to instruction manuals.

[0316] An "inquiry" refers to information that users use to communicate unclear points or questions that arise when referring to instructions to the server.

[0317] "An artificial intelligence method for evaluating knowledge levels" refers to a technology that analyzes the content of inquiries received from users and measures the specific knowledge and level of understanding that users possess.

[0318] A "means for evaluating emotional state" refers to a technology that analyzes a user's input and actions to determine and evaluate their emotional state.

[0319] "Means of providing customized solutions" refers to the techniques and processes for appropriately adjusting the content of standard instructions based on the user's knowledge level and emotional state, and providing them in the most optimal form for the user.

[0320] This invention is a system for providing standard instructions in a way that is easy for users to understand and that takes their emotional state into consideration. The system is broadly composed of a server, a terminal, and the user.

[0321] The server receives standard instruction manuals from users (instruction manual creators) via their terminals and stores them in appropriate databases. During this process, data management software such as MySQL or MongoDB is used to organize and store metadata. Users (instruction manual users) can access these databases and retrieve the necessary instruction manuals.

[0322] If a user has a question while referring to the instructions, they can input the question through an interface on their terminal and send it to the server. The server receives this inquiry and analyzes the user's knowledge level using a generative AI model. This process utilizes natural language processing tools and models, such as OpenAI's language model.

[0323] Furthermore, the emotion engine evaluates the user's emotional state based on their input. This evaluation uses text analysis technology to infer emotions from factors such as input speed and phrasing patterns. Based on this information, the server appropriately customizes the standard instructions. Specific examples include adding explanations of technical terms and using examples in more user-friendly language. In addition, if the system determines that the user is experiencing high stress levels, it may add encouraging messages.

[0324] Finally, the customized instructions are temporarily stored on the server's storage and sent to the user's terminal. At this point, the terminal displays the instructions on the screen in an appropriate format. The system also uses prompts to optimize the instructions according to the user's state. For example, a prompt might read: "Please provide clearer explanations and simpler examples for the operating procedures that the user finds difficult. Also, please add words of encouragement, as the user may be feeling stressed."

[0325] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0326] Step 1:

[0327] The user creates a standard instruction manual and uploads the standard instruction manual file to the server via a terminal. The input is a digital file of the instruction manual. Specifically, the user selects the file through a dedicated interface and presses the send button. The output is a standard instruction manual file saved on the server.

[0328] Step 2:

[0329] The server stores the received standard instructions in a database. The input is a digital file uploaded by the user, and metadata is automatically generated and associated processing is performed when storing it in the database. Specifically, information including the file name, creation date, and creator is tagged. The output is the standard instruction data recorded in the database.

[0330] Step 3:

[0331] If a user has difficulty understanding the instructions, they can enter a question via their terminal and send it to the server. The input is a free-form question text entered by the user. Specifically, the user enters the question into the text box on the terminal and presses the send button. The output at this point is the question text that is sent to the server.

[0332] Step 4:

[0333] The server analyzes received questions using a generative AI model. The input is the user's question text, which is tokenized to extract important keywords. Subsequently, the server evaluates the user's knowledge level based on this. Specifically, the analysis results are scored to determine how specialized the knowledge is. The output is evaluation data indicating the user's knowledge level.

[0334] Step 5:

[0335] The server uses an emotion engine to evaluate the user's emotional state. Inputs include the user's question text and input speed, and the server determines emotions through text analysis. Specifically, it applies algorithms to measure positive and negative emotions and evaluates multiple emotions as needed. The output is data that explicitly indicates the user's emotional state.

[0336] Step 6:

[0337] The server customizes the standard instructions based on the knowledge level and emotional state assessment obtained in the previous step. The inputs are the assessment data and the original standard instructions. Specifically, the server inserts additional explanations and examples into the instructions and modifies the text tone to suit the user. The output is the customized instructions.

[0338] Step 7:

[0339] The server temporarily stores the customized instructions in storage and then sends them to the user's terminal. The input is the customized instruction data, and the output is the instructions displayed on the user's terminal. Specifically, the server verifies the integrity of the data before sending it to the terminal using a communication protocol.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0342] The challenge lies in reducing the stress users experience when using work instructions, particularly those related to understanding and emotions, and thereby improving work efficiency. In particular, in factory and other on-site work environments, there is a lack of instructions tailored to the workers' level of understanding, as well as insufficient emotional support.

[0343] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0344] In this invention, the server includes a device for recording standard work instructions, a device for receiving inquiries from users, an artificial intelligence device for evaluating the user's level of understanding, an emotion recognition device for recognizing and evaluating the user's emotional state, and a device for customizing and providing instructions based on the level of understanding and emotional state. This makes it possible to provide instructions optimized for the user's knowledge level and emotions, thereby reducing the burden on workers and improving work efficiency.

[0345] A "standard work instruction sheet" is a document that describes the procedures and precautions for performing a specific task, and is usually referred to when carrying out work.

[0346] A "device" is a collection of technical components designed to perform a specific function, and includes hardware and software for performing specific operations or processes.

[0347] An "inquiry" refers to a question or request made by a user to obtain unclear points or specific information, and is an action of seeking information from a system.

[0348] "Artificial intelligence" refers to the technology of computer systems that mimic human intellectual behavior and logical reasoning, and specifically to the ability to analyze data and make inferences and judgments.

[0349] "Emotion recognition" is a technology that analyzes and judges a user's emotional state, evaluating the user's mental state from factors such as voice, facial expressions, and behavior.

[0350] "Customization" refers to modifying content to suit the specific needs and circumstances of a particular user, thereby adjusting the content to make it easier for the user to understand and use.

[0351] The system implementing this invention supports users in efficiently utilizing work instructions. The system operates using a terminal that records standard work instructions and receives inquiries from users. The server performs artificial intelligence (AI) processing to analyze user inquiries and thereby evaluate the user's knowledge level. Generative AI models such as OpenAI are used for this purpose.

[0352] Furthermore, by using an emotion recognition device, the system determines the user's emotional state from their facial expressions and tone of voice. For this purpose, for example, Microsoft's Emotion API is utilized. Based on this evaluated information, the server customizes the work instructions in a way that is optimized for the user. This customization includes not only simplifying the terminology in the instructions but also adding encouraging and reassuring messages.

[0353] As a concrete example, consider a scenario where a user uses smart glasses to refer to work instructions. If the user makes a mistake during the task, the glasses detect it and send a query to the server. The server then assesses the user's understanding and emotional state and provides instructions that gently suggest specific next steps. This might be a message like, "You've got the first step down! Now let's try this."

[0354] Examples of prompt statements for a generative AI model are as follows:

[0355] User question: "I don't know where to install this part."

[0356] Prompt message: "The user has accessed the instruction manual page and asked a question about the installation location of a part. The user's knowledge level is intermediate, and they appear slightly stressed. Please provide clear and encouraging instructions."

[0357] This system allows users to receive appropriate guidance based on their level of understanding and emotional needs, enabling them to acquire job skills effectively and efficiently.

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The user uses a terminal to refer to work instructions and enters any questions or points of confusion during the process. The entered inquiries are then sent from the terminal to the server.

[0361] Step 2:

[0362] The server analyzes the received inquiry and uses a generative AI model to determine the user's level of understanding. The user's question is used as input, and the user's knowledge level is output as the analysis result.

[0363] Step 3:

[0364] A separate device, an emotion recognition device, detects the user's facial expressions and voice data and analyzes their emotional state. Here, the user's real-time images and voice are used as input, and the user's emotional state is obtained as output.

[0365] Step 4:

[0366] The server integrates the results of the generated AI model and the emotion recognition results to customize the optimal instructions according to the user's level of understanding and emotional state. The input is the aforementioned analysis results, and the output is a customized set of instructions.

[0367] Step 5:

[0368] The customized instructions are temporarily stored on the server. They are then converted to the required format and sent to the terminal.

[0369] Step 6:

[0370] The terminal displays the received customization instructions to the user. The user can then proceed with the work based on the instructions. As a result, the user's work efficiency improves.

[0371] 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.

[0372] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0374] [Third Embodiment]

[0375] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0376] 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.

[0377] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0378] 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.

[0379] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0380] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0381] 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.

[0382] 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.

[0383] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0384] The 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.

[0385] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0386] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0387] This invention is a system for customizing standard work instructions to suit each user's level of understanding. The embodiments thereof are described in detail below.

[0388] The system begins with a user (manual creator) creating standard work instructions related to their tasks and uploading them to the server via a terminal. The server then stores these instructions in a database, making them accessible to all users.

[0389] Users (manual users) access and learn from this standard operating instructions. If they have any questions or need further details, they enter their inquiries via their terminal and send them to the server. Based on the received inquiries, the server uses artificial intelligence to assess the user's knowledge level.

[0390] Artificial intelligence analyzes the frequency and complexity of the terminology used by the user to determine the user's level of understanding. Once the evaluation is complete, the server customizes the standard operating instructions based on the information provided by the AI. This customization may involve reorganizing the instructions to include simpler explanations, additional examples, and diagrams.

[0391] Once customized, the instruction manual is temporarily stored on the server and provided to the user via the terminal. This allows users to efficiently learn using instruction manuals optimized for their individual level and acquire the skills necessary for their work.

[0392] As a concrete example, when a user learns how to use a new tool for a particular task, they refer to the standard instruction manual, and if they have an insufficient understanding of a specific function, they post a question about that part. The server analyzes the question and determines that the user understands the basic operations but needs further information on advanced settings. Based on this determination, the server generates a customized manual that includes detailed procedures for advanced settings, along with concise and easy-to-understand explanations, and provides it to the user.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] The user (manual creator) creates a work instruction sheet and uploads it to the server via their terminal. The server then stores the received data in a database.

[0396] Step 2:

[0397] Users (manual users) access saved standard operating instructions and learn from their contents. They can input questions via their terminal about areas where their understanding is lacking or where they want more detailed information, and send these questions to the server.

[0398] Step 3:

[0399] The server receives questions from users and sends their content to an artificial intelligence model. The AI ​​analyzes the questions and evaluates the user's knowledge level and level of understanding.

[0400] Step 4:

[0401] The generating AI determines the user's level of understanding based on the frequency and complexity of terms in the question. Based on the determination, it provides the server with information on how detailed the information needs to be.

[0402] Step 5:

[0403] The server customizes the standard work instructions based on the judgment results provided by the artificial intelligence. The customized instructions include additional detailed explanations, specific examples, and diagrams.

[0404] Step 6:

[0405] The customized instructions are temporarily saved and then sent to the user. The user can receive this information on their device and continue learning.

[0406] (Example 1)

[0407] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0408] Existing standard instruction manuals are difficult to customize to suit each user's level of understanding, resulting in challenges for users to efficiently and effectively acquire the necessary skills. In particular, there is a lack of methods to individually improve instruction manuals based on user inquiries and feedback, thus maximizing the effectiveness of learning.

[0409] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0410] In this invention, the server includes means for recording instruction materials, means for receiving inquiries from users, artificial intelligence means for evaluating the user's level of understanding from the inquiries, means for improving and providing the instruction materials based on the evaluation, and means for temporarily storing the improved instruction materials and performing preprocessing before providing them to the user. This makes it possible to provide individually optimized instruction materials according to each user's level of understanding, thereby supporting efficient and effective learning.

[0411] "Instruction materials" are documents that describe work procedures and operating methods, and represent the standard workflow for a given task.

[0412] "User" refers to an individual who uses instruction materials to perform tasks, or an individual who creates instruction materials to provide information.

[0413] "Means of receiving inquiries" refers to the method by which users can send questions or points of confusion regarding instruction materials to the server and receive them.

[0414] "An artificial intelligence method for evaluating comprehension" refers to artificial intelligence technology used to analyze the content of user inquiries and determine the user's knowledge level and level of comprehension based on that content.

[0415] "Methods of providing improved information" refers to a method of adjusting instructional materials to the user's level based on their level of understanding, as assessed by artificial intelligence, and providing optimized information.

[0416] "Means for temporarily storing and pre-processing information" refers to methods for temporarily storing improved instructional materials and presenting them to the user in the appropriate format when needed.

[0417] A "generative AI model" is an artificial intelligence model used to analyze a user's level of understanding, and refers to a technology that deeply understands and evaluates the content of a question.

[0418] This invention is a system that customizes instruction materials according to each user's level of understanding. Specifically, it begins with creating instruction materials using a terminal and uploading them to a server. The hardware used includes personal computers and tablet devices operated by the user, and the software includes document creation tools and web browsers. On the server side, a database management system and artificial intelligence models, particularly generative AI models, are provided. This allows the server to store instruction materials in a database and facilitate smooth communication with users.

[0419] When a user accesses instructional materials and progresses through their learning, they can submit inquiries through their device if they have questions or want more details. These inquiries are sent via natural language input and received by the server. The server utilizes a generative AI model to analyze the received inquiries. This model evaluates the frequency and complexity of the terms used by the user and determines their level of understanding. Prompts such as "Analyze this user's question and evaluate their level of understanding" are used.

[0420] Based on the assessed level of understanding, the server customizes the instruction materials. This includes using clearer language and adding diagrams and examples as needed. Furthermore, for areas requiring specialized knowledge, it can add more detailed steps. For example, when learning how to use a new software tool, instructions are generated that cover everything from basic to advanced settings, tailored to the user's skill level.

[0421] The generated customized instructions are temporarily stored on the server and provided to the user via their terminal. This allows users to efficiently proceed with learning and practical work using materials optimized to their level of understanding.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] Users create instruction documents related to their work and upload them to the server using their terminal. The input is the instruction document file created by the user (e.g., PDF, Word format). The server stores the received documents in a database and records the relevant metadata. As output, an access link to the instruction document is generated.

[0425] Step 2:

[0426] The user accesses the server through their device and views the stored instruction materials. The input is the user's access request, and the server processes the viewing request and provides the corresponding instruction materials. The output is the instruction materials displayed on the user's device.

[0427] Step 3:

[0428] The user refers to the instruction materials, enters inquiries about parts they are unsure of or would like more details about, and sends them to the server via their terminal. The input is the user's inquiry, and the output is the inquiry data received by the server.

[0429] Step 4:

[0430] The server analyzes the received query and uses a generative AI model to evaluate the user's understanding. The input is user query data, and the server performs data calculations by analyzing the frequency and complexity of terms. The output is the user understanding evaluation result.

[0431] Step 5:

[0432] The server customizes the instruction materials based on the comprehension assessment results. The input consists of the user's comprehension assessment results and the original instruction materials. With the help of a generative AI model, the server processes the data by adjusting wording and content, and adding necessary diagrams and examples. The output is the customized instruction materials.

[0433] Step 6:

[0434] The server temporarily stores customized instruction materials and provides them to the user via the terminal. The input is the customized instruction materials, and the output is a download or display link provided to the user's terminal. This allows the user to access materials appropriate to their level and learn efficiently.

[0435] (Application Example 1)

[0436] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] While factories require workers with varying skill levels to perform tasks efficiently, current work instruction information is insufficiently customized to each worker's level of understanding, resulting in ineffective guidance for diverse workforces. Furthermore, the limited methods for obtaining necessary information on the spot during work raise concerns about decreased work efficiency.

[0438] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0439] In this invention, the server includes a device for recording standard work instruction information, a device for receiving inquiries from users, and a knowledge processing device for evaluating the user's level of understanding from the inquiries. This makes it possible to provide work instruction information adapted to each worker's level of understanding in real time via a visual display device.

[0440] "Standard work instructions" are documents that describe the procedures and methods that workers should follow in workplaces such as factories, and are used to ensure the efficiency and safety of each task.

[0441] "User" refers to an individual or group responsible for referring to standard work instruction information and performing work based on it.

[0442] An "inquiry" refers to a question or request that a user submits regarding work instruction information if they have a misunderstanding or require additional explanation.

[0443] A "knowledge processing device" is a device that uses artificial intelligence technology to analyze user inquiries and evaluate the user's level of understanding.

[0444] A "visual display device" is a device that visualizes information to the user in real time via devices such as smart glasses.

[0445] "Real-time delivery" means providing necessary information with minimal interruption to work by instantly displaying customized work instructions in response to user inquiries.

[0446] This invention is a system that customizes standard work instruction information according to each worker's level of understanding and provides it in real time, enabling workers in a factory to perform their tasks efficiently. The main components of the system include a server, a knowledge processing device, and a visual display device. Its detailed operation and usage are described below.

[0447] First, the server records standard work instruction information in a digital format and stores it in a database. This database must be accessible to all workers in the factory. When a question arises, users send the content to the server via an input device. This input device may include devices with built-in voice recognition capabilities.

[0448] The server receives this query and uses a knowledge processing unit to analyze the data. The knowledge processing unit leverages a generative AI model to evaluate the user's understanding based on the frequency and complexity of the terms included in the query. This process utilizes machine learning libraries such as TensorFlow.

[0449] Based on the evaluation results, the server generates customized standard work instruction information. This customized information is provided to the user in real time through a visual display device. Smart glasses or head-mounted displays are used as visual display devices.

[0450] For example, when setting up a new machine, if an operator verbally inputs "I don't know how to operate this function," the system converts that information into text and analyzes it using a knowledge processing device. As a result, detailed operating procedures and video manuals tailored to the operator's level of understanding are customized and displayed on the smart glasses' screen.

[0451] An example of a prompt message for a generated AI model is, "Please customize and provide details regarding the setup procedure for the new machine."

[0452] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0453] Step 1:

[0454] The user accesses standard work instruction information through a terminal. The input here is the type of work instruction information the user requires, and the output is digitized standard work instruction information. The terminal retrieves and displays the information from the server.

[0455] Step 2:

[0456] Users input inquiries into the terminal in voice or text format regarding parts they find difficult to understand or where additional explanations are needed. These inquiries constitute the input data and are sent to the server. The server then forwards this data to the knowledge processing unit.

[0457] Step 3:

[0458] The server analyzes the query using a knowledge processing device. Here, the input data is the query content, and the output is the user's comprehension evaluation result. The knowledge processing device analyzes the frequency and complexity of terminology through a generative AI model and evaluates the user's comprehension level.

[0459] Step 4:

[0460] The server customizes standard work instruction information based on the evaluation results. The input here is the comprehension evaluation result, and the output is the customized work instruction information. The server adds explanations and details that match the evaluation and restructures the information.

[0461] Step 5:

[0462] The server sends customized work instruction information to a visual display device. The input is customized information, and the output is information displayed in real time on the user's visual display device. The terminal displays this information, allowing the user to continue working.

[0463] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0464] This invention combines a system that customizes work instructions for each user with an emotion engine that recognizes the user's emotions. The aim of this system is to comprehensively evaluate the user's level of understanding and emotions and provide instructions optimized for the user.

[0465] The system first allows the user (manual creator) to create a standard work instruction document and upload it to the server via a terminal. The server then stores this instruction document in a database, making it accessible to the user (manual user).

[0466] The user refers to the instructions, and if there are any unclear points, they input a question through their terminal and send it to the server. The server analyzes the user's question using a generating AI to determine the user's knowledge level.

[0467] In addition, the emotion engine recognizes the user's emotional state through their words and actions. This information is analyzed from, for example, the phrasing of text, the content of repeated questions, and the typing speed.

[0468] After assessing the user's knowledge level and emotional state, the server uses this data to customize the instructions in a way that best suits the user. Specifically, it may add explanations of technical terms or include more easily understandable examples. Furthermore, if the user is likely to be under stress, the server may add kind words and encouraging messages to the instructions, helping to reduce the user's work pressure.

[0469] Once customized, the instructions are temporarily stored on the server before being sent to the user's terminal. This allows users to refer to instructions tailored to their level of understanding and preferences, enabling them to learn and perform tasks more efficiently.

[0470] For example, if a user is learning how to use new software and the operating procedures are complex and stressful, the system will simplify the instructions and add explanations in user-friendly language. Providing information tailored to the user's situation can improve the efficiency of learning new skills.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user (manual creator) uses a terminal to create a standard work instruction sheet and uploads it to the server. The server then stores it in its database.

[0474] Step 2:

[0475] The user (manual user) accesses the database via a terminal and views the standard operating instructions. If there are any parts that are difficult to understand while viewing, they can input and send questions from the terminal.

[0476] Step 3:

[0477] The server receives questions from users and sends that data to a generating AI. The generating AI analyzes the content of the questions and evaluates the user's knowledge level.

[0478] Step 4:

[0479] An emotion engine running on the device analyzes the user's input speed, keyboard operation, and expressions to recognize the user's emotional state.

[0480] Step 5:

[0481] The server customizes standard work instructions for the user based on evaluation results from the generating AI and emotional states from the emotion engine. For example, it adds detailed explanations to complex operations and inserts relaxing language if the user is feeling stressed.

[0482] Step 6:

[0483] The server temporarily stores the customized instructions in storage and sends them to the terminal. The terminal displays the instructions to the user, who then continues their work while referring to them.

[0484] Step 7:

[0485] The goal is to ensure that users receive updated instructions and can proceed with their work with confidence, based on information that aligns with their understanding and emotional needs.

[0486] (Example 2)

[0487] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] Conventional instruction management systems often fail to provide instructions in a format that is easy for users to understand and does not take into account their emotional state, which can hinder efficient work execution and learning. This can lead to increased user stress and a decline in the quality of work and learning speed.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0490] In this invention, the server includes means for recording standard instructions, means for receiving inquiries from users, and artificial intelligence means for evaluating the user's knowledge level and emotional state. This makes it possible to provide instructions customized according to the user's level of understanding and emotional state.

[0491] A "standard instruction manual" is a document that describes the operating procedures or steps that are generally adopted for a particular task or operation.

[0492] "User" refers to a person who operates the system to create or refer to instruction manuals.

[0493] An "inquiry" refers to information that users use to communicate unclear points or questions that arise when referring to instructions to the server.

[0494] "An artificial intelligence method for evaluating knowledge levels" refers to a technology that analyzes the content of inquiries received from users and measures the specific knowledge and level of understanding that users possess.

[0495] A "means for evaluating emotional state" refers to a technology that analyzes a user's input and actions to determine and evaluate their emotional state.

[0496] "Means of providing customized solutions" refers to the techniques and processes for appropriately adjusting the content of standard instructions based on the user's knowledge level and emotional state, and providing them in the most optimal form for the user.

[0497] This invention is a system for providing standard instructions in a way that is easy for users to understand and that takes their emotional state into consideration. The system is broadly composed of a server, a terminal, and the user.

[0498] The server receives standard instruction manuals from users (instruction manual creators) via their terminals and stores them in appropriate databases. During this process, data management software such as MySQL or MongoDB is used to organize and store metadata. Users (instruction manual users) can access these databases and retrieve the necessary instruction manuals.

[0499] If a user has a question while referring to the instructions, they can input the question through an interface on their terminal and send it to the server. The server receives this inquiry and analyzes the user's knowledge level using a generative AI model. This process utilizes natural language processing tools and models, such as OpenAI's language model.

[0500] Furthermore, the emotion engine evaluates the user's emotional state based on their input. This evaluation uses text analysis technology to infer emotions from factors such as input speed and phrasing patterns. Based on this information, the server appropriately customizes the standard instructions. Specific examples include adding explanations of technical terms and using examples in more user-friendly language. In addition, if the system determines that the user is experiencing high stress levels, it may add encouraging messages.

[0501] Finally, the customized instructions are temporarily stored on the server's storage and sent to the user's terminal. At this point, the terminal displays the instructions on the screen in an appropriate format. The system also uses prompts to optimize the instructions according to the user's state. For example, a prompt might read: "Please provide clearer explanations and simpler examples for the operating procedures that the user finds difficult. Also, please add words of encouragement, as the user may be feeling stressed."

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The user creates a standard instruction manual and uploads the standard instruction manual file to the server via a terminal. The input is a digital file of the instruction manual. Specifically, the user selects the file through a dedicated interface and presses the send button. The output is a standard instruction manual file saved on the server.

[0505] Step 2:

[0506] The server stores the received standard instructions in a database. The input is a digital file uploaded by the user, and metadata is automatically generated and associated processing is performed when storing it in the database. Specifically, information including the file name, creation date, and creator is tagged. The output is the standard instruction data recorded in the database.

[0507] Step 3:

[0508] If a user has difficulty understanding the instructions, they can enter a question via their terminal and send it to the server. The input is a free-form question text entered by the user. Specifically, the user enters the question into the text box on the terminal and presses the send button. The output at this point is the question text that is sent to the server.

[0509] Step 4:

[0510] The server analyzes received questions using a generative AI model. The input is the user's question text, which is tokenized to extract important keywords. Subsequently, the server evaluates the user's knowledge level based on this. Specifically, the analysis results are scored to determine how specialized the knowledge is. The output is evaluation data indicating the user's knowledge level.

[0511] Step 5:

[0512] The server uses an emotion engine to evaluate the user's emotional state. Inputs include the user's question text and input speed, and the server determines emotions through text analysis. Specifically, it applies algorithms to measure positive and negative emotions and evaluates multiple emotions as needed. The output is data that explicitly indicates the user's emotional state.

[0513] Step 6:

[0514] The server customizes the standard instructions based on the knowledge level and emotional state assessment obtained in the previous step. The inputs are the assessment data and the original standard instructions. Specifically, the server inserts additional explanations and examples into the instructions and modifies the text tone to suit the user. The output is the customized instructions.

[0515] Step 7:

[0516] The server temporarily stores the customized instructions in storage and then sends them to the user's terminal. The input is the customized instruction data, and the output is the instructions displayed on the user's terminal. Specifically, the server verifies the integrity of the data before sending it to the terminal using a communication protocol.

[0517] (Application Example 2)

[0518] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] The challenge lies in reducing the stress users experience when using work instructions, particularly those related to understanding and emotions, and thereby improving work efficiency. In particular, in factory and other on-site work environments, there is a lack of instructions tailored to the workers' level of understanding, as well as insufficient emotional support.

[0520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0521] In this invention, the server includes a device for recording standard work instructions, a device for receiving inquiries from users, an artificial intelligence device for evaluating the user's level of understanding, an emotion recognition device for recognizing and evaluating the user's emotional state, and a device for customizing and providing instructions based on the level of understanding and emotional state. This makes it possible to provide instructions optimized for the user's knowledge level and emotions, thereby reducing the burden on workers and improving work efficiency.

[0522] A "standard work instruction sheet" is a document that describes the procedures and precautions for performing a specific task, and is usually referred to when carrying out work.

[0523] A "device" is a collection of technical components designed to perform a specific function, and includes hardware and software for performing specific operations or processes.

[0524] An "inquiry" refers to a question or request made by a user to obtain unclear points or specific information, and is an action of seeking information from a system.

[0525] "Artificial intelligence" refers to the technology of computer systems that mimic human intellectual behavior and logical reasoning, and specifically to the ability to analyze data and make inferences and judgments.

[0526] "Emotion recognition" is a technology that analyzes and judges a user's emotional state, evaluating the user's mental state from factors such as voice, facial expressions, and behavior.

[0527] "Customization" refers to modifying content to suit the specific needs and circumstances of a particular user, thereby adjusting the content to make it easier for the user to understand and use.

[0528] The system implementing this invention supports users in efficiently utilizing work instructions. The system operates using a terminal that records standard work instructions and receives inquiries from users. The server performs artificial intelligence (AI) processing to analyze user inquiries and thereby evaluate the user's knowledge level. Generative AI models such as OpenAI are used for this purpose.

[0529] Furthermore, by using an emotion recognition device, the system determines the user's emotional state from their facial expressions and tone of voice. For this purpose, for example, Microsoft's Emotion API is utilized. Based on this evaluated information, the server customizes the work instructions in a way that is optimized for the user. This customization includes not only simplifying the terminology in the instructions but also adding encouraging and reassuring messages.

[0530] As a concrete example, consider a scenario where a user uses smart glasses to refer to work instructions. If the user makes a mistake during the task, the glasses detect it and send a query to the server. The server then assesses the user's understanding and emotional state and provides instructions that gently suggest specific next steps. This might be a message like, "You've got the first step down! Now let's try this."

[0531] Examples of prompt statements for a generative AI model are as follows:

[0532] User question: "I don't know where to install this part."

[0533] Prompt message: "The user has accessed the instruction manual page and asked a question about the installation location of a part. The user's knowledge level is intermediate, and they appear slightly stressed. Please provide clear and encouraging instructions."

[0534] This system allows users to receive appropriate guidance based on their level of understanding and emotional needs, enabling them to acquire job skills effectively and efficiently.

[0535] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0536] Step 1:

[0537] The user uses a terminal to refer to work instructions and enters any questions or points of confusion during the process. The entered inquiries are then sent from the terminal to the server.

[0538] Step 2:

[0539] The server analyzes the received inquiry and uses a generative AI model to determine the user's level of understanding. The user's question is used as input, and the user's knowledge level is output as the analysis result.

[0540] Step 3:

[0541] A separate device, an emotion recognition device, detects the user's facial expressions and voice data and analyzes their emotional state. Here, the user's real-time images and voice are used as input, and the user's emotional state is obtained as output.

[0542] Step 4:

[0543] The server integrates the results of the generated AI model and the emotion recognition results to customize the optimal instructions according to the user's level of understanding and emotional state. The input is the aforementioned analysis results, and the output is a customized set of instructions.

[0544] Step 5:

[0545] The customized instructions are temporarily stored on the server. They are then converted to the required format and sent to the terminal.

[0546] Step 6:

[0547] The terminal displays the received customization instructions to the user. The user can then proceed with the work based on the instructions. As a result, the user's work efficiency improves.

[0548] 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.

[0549] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0550] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0551] [Fourth Embodiment]

[0552] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0553] 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.

[0554] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0555] 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.

[0556] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0557] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0558] 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.

[0559] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0560] 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.

[0561] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0562] The 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.

[0563] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0564] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0565] This invention is a system for customizing standard work instructions to suit each user's level of understanding. The embodiments thereof are described in detail below.

[0566] The system begins with a user (manual creator) creating standard work instructions related to their tasks and uploading them to the server via a terminal. The server then stores these instructions in a database, making them accessible to all users.

[0567] Users (manual users) access and learn from this standard operating instructions. If they have any questions or need further details, they enter their inquiries via their terminal and send them to the server. Based on the received inquiries, the server uses artificial intelligence to assess the user's knowledge level.

[0568] Artificial intelligence analyzes the frequency and complexity of the terminology used by the user to determine the user's level of understanding. Once the evaluation is complete, the server customizes the standard operating instructions based on the information provided by the AI. This customization may involve reorganizing the instructions to include simpler explanations, additional examples, and diagrams.

[0569] Once customized, the instruction manual is temporarily stored on the server and provided to the user via the terminal. This allows users to efficiently learn using instruction manuals optimized for their individual level and acquire the skills necessary for their work.

[0570] As a concrete example, when a user learns how to use a new tool for a particular task, they refer to the standard instruction manual, and if they have an insufficient understanding of a specific function, they post a question about that part. The server analyzes the question and determines that the user understands the basic operations but needs further information on advanced settings. Based on this determination, the server generates a customized manual that includes detailed procedures for advanced settings, along with concise and easy-to-understand explanations, and provides it to the user.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The user (manual creator) creates a work instruction sheet and uploads it to the server via their terminal. The server then stores the received data in a database.

[0574] Step 2:

[0575] Users (manual users) access saved standard operating instructions and learn from their contents. They can input questions via their terminal about areas where their understanding is lacking or where they want more detailed information, and send these questions to the server.

[0576] Step 3:

[0577] The server receives questions from users and sends their content to an artificial intelligence model. The AI ​​analyzes the questions and evaluates the user's knowledge level and level of understanding.

[0578] Step 4:

[0579] The generating AI determines the user's level of understanding based on the frequency and complexity of terms in the question. Based on the determination, it provides the server with information on how detailed the information needs to be.

[0580] Step 5:

[0581] The server customizes the standard work instructions based on the judgment results provided by the artificial intelligence. The customized instructions include additional detailed explanations, specific examples, and diagrams.

[0582] Step 6:

[0583] The customized instructions are temporarily saved and then sent to the user. The user can receive this information on their device and continue learning.

[0584] (Example 1)

[0585] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0586] Existing standard instruction manuals are difficult to customize to suit each user's level of understanding, resulting in challenges for users to efficiently and effectively acquire the necessary skills. In particular, there is a lack of methods to individually improve instruction manuals based on user inquiries and feedback, thus maximizing the effectiveness of learning.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0588] In this invention, the server includes means for recording instruction materials, means for receiving inquiries from users, artificial intelligence means for evaluating the user's level of understanding from the inquiries, means for improving and providing the instruction materials based on the evaluation, and means for temporarily storing the improved instruction materials and performing preprocessing before providing them to the user. This makes it possible to provide individually optimized instruction materials according to each user's level of understanding, thereby supporting efficient and effective learning.

[0589] "Instruction materials" are documents that describe work procedures and operating methods, and represent the standard workflow for a given task.

[0590] "User" refers to an individual who uses instruction materials to perform tasks, or an individual who creates instruction materials to provide information.

[0591] "Means of receiving inquiries" refers to the method by which users can send questions or points of confusion regarding instruction materials to the server and receive them.

[0592] "An artificial intelligence method for evaluating comprehension" refers to artificial intelligence technology used to analyze the content of user inquiries and determine the user's knowledge level and level of comprehension based on that content.

[0593] "Methods of providing improved information" refers to a method of adjusting instructional materials to the user's level based on their level of understanding, as assessed by artificial intelligence, and providing optimized information.

[0594] "Means for temporarily storing and pre-processing information" refers to methods for temporarily storing improved instructional materials and presenting them to the user in the appropriate format when needed.

[0595] A "generative AI model" is an artificial intelligence model used to analyze a user's level of understanding, and refers to a technology that deeply understands and evaluates the content of a question.

[0596] This invention is a system that customizes instruction materials according to each user's level of understanding. Specifically, it begins with creating instruction materials using a terminal and uploading them to a server. The hardware used includes personal computers and tablet devices operated by the user, and the software includes document creation tools and web browsers. On the server side, a database management system and artificial intelligence models, particularly generative AI models, are provided. This allows the server to store instruction materials in a database and facilitate smooth communication with users.

[0597] When a user accesses instructional materials and progresses through their learning, they can submit inquiries through their device if they have questions or want more details. These inquiries are sent via natural language input and received by the server. The server utilizes a generative AI model to analyze the received inquiries. This model evaluates the frequency and complexity of the terms used by the user and determines their level of understanding. Prompts such as "Analyze this user's question and evaluate their level of understanding" are used.

[0598] Based on the assessed level of understanding, the server customizes the instruction materials. This includes using clearer language and adding diagrams and examples as needed. Furthermore, for areas requiring specialized knowledge, it can add more detailed steps. For example, when learning how to use a new software tool, instructions are generated that cover everything from basic to advanced settings, tailored to the user's skill level.

[0599] The generated customized instructions are temporarily stored on the server and provided to the user via their terminal. This allows users to efficiently proceed with learning and practical work using materials optimized to their level of understanding.

[0600] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0601] Step 1:

[0602] Users create instruction documents related to their work and upload them to the server using their terminal. The input is the instruction document file created by the user (e.g., PDF, Word format). The server stores the received documents in a database and records the relevant metadata. As output, an access link to the instruction document is generated.

[0603] Step 2:

[0604] The user accesses the server through their device and views the stored instruction materials. The input is the user's access request, and the server processes the viewing request and provides the corresponding instruction materials. The output is the instruction materials displayed on the user's device.

[0605] Step 3:

[0606] The user refers to the instruction materials, enters inquiries about parts they are unsure of or would like more details about, and sends them to the server via their terminal. The input is the user's inquiry, and the output is the inquiry data received by the server.

[0607] Step 4:

[0608] The server analyzes the received query and uses a generative AI model to evaluate the user's understanding. The input is user query data, and the server performs data calculations by analyzing the frequency and complexity of terms. The output is the user understanding evaluation result.

[0609] Step 5:

[0610] The server customizes the instruction materials based on the comprehension assessment results. The input consists of the user's comprehension assessment results and the original instruction materials. With the help of a generative AI model, the server processes the data by adjusting wording and content, and adding necessary diagrams and examples. The output is the customized instruction materials.

[0611] Step 6:

[0612] The server temporarily stores customized instruction materials and provides them to the user via the terminal. The input is the customized instruction materials, and the output is a download or display link provided to the user's terminal. This allows the user to access materials appropriate to their level and learn efficiently.

[0613] (Application Example 1)

[0614] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] While factories require workers with varying skill levels to perform tasks efficiently, current work instruction information is insufficiently customized to each worker's level of understanding, resulting in ineffective guidance for diverse workforces. Furthermore, the limited methods for obtaining necessary information on the spot during work raise concerns about decreased work efficiency.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0617] In this invention, the server includes a device for recording standard work instruction information, a device for receiving inquiries from users, and a knowledge processing device for evaluating the user's level of understanding from the inquiries. This makes it possible to provide work instruction information adapted to each worker's level of understanding in real time via a visual display device.

[0618] "Standard work instructions" are documents that describe the procedures and methods that workers should follow in workplaces such as factories, and are used to ensure the efficiency and safety of each task.

[0619] "User" refers to an individual or group responsible for referring to standard work instruction information and performing work based on it.

[0620] An "inquiry" refers to a question or request that a user submits regarding work instruction information if they have a misunderstanding or require additional explanation.

[0621] A "knowledge processing device" is a device that uses artificial intelligence technology to analyze user inquiries and evaluate the user's level of understanding.

[0622] A "visual display device" is a device that visualizes information to the user in real time via devices such as smart glasses.

[0623] "Real-time delivery" means providing necessary information with minimal interruption to work by instantly displaying customized work instructions in response to user inquiries.

[0624] This invention is a system that customizes standard work instruction information according to each worker's level of understanding and provides it in real time, enabling workers in a factory to perform their tasks efficiently. The main components of the system include a server, a knowledge processing device, and a visual display device. Its detailed operation and usage are described below.

[0625] First, the server records standard work instruction information in a digital format and stores it in a database. This database must be accessible to all workers in the factory. When a question arises, users send the content to the server via an input device. This input device may include devices with built-in voice recognition capabilities.

[0626] The server receives this query and uses a knowledge processing unit to analyze the data. The knowledge processing unit leverages a generative AI model to evaluate the user's understanding based on the frequency and complexity of the terms included in the query. This process utilizes machine learning libraries such as TensorFlow.

[0627] Based on the evaluation results, the server generates customized standard work instruction information. This customized information is provided to the user in real time through a visual display device. Smart glasses or head-mounted displays are used as visual display devices.

[0628] For example, when setting up a new machine, if an operator verbally inputs "I don't know how to operate this function," the system converts that information into text and analyzes it using a knowledge processing device. As a result, detailed operating procedures and video manuals tailored to the operator's level of understanding are customized and displayed on the smart glasses' screen.

[0629] An example of a prompt message for a generated AI model is, "Please customize and provide details regarding the setup procedure for the new machine."

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The user accesses standard work instruction information through a terminal. The input here is the type of work instruction information the user requires, and the output is digitized standard work instruction information. The terminal retrieves and displays the information from the server.

[0633] Step 2:

[0634] Users input inquiries into the terminal in voice or text format regarding parts they find difficult to understand or where additional explanations are needed. These inquiries constitute the input data and are sent to the server. The server then forwards this data to the knowledge processing unit.

[0635] Step 3:

[0636] The server analyzes the query using a knowledge processing device. Here, the input data is the query content, and the output is the user's comprehension evaluation result. The knowledge processing device analyzes the frequency and complexity of terminology through a generative AI model and evaluates the user's comprehension level.

[0637] Step 4:

[0638] The server customizes standard work instruction information based on the evaluation results. The input here is the comprehension evaluation result, and the output is the customized work instruction information. The server adds explanations and details that match the evaluation and restructures the information.

[0639] Step 5:

[0640] The server sends customized work instruction information to a visual display device. The input is customized information, and the output is information displayed in real time on the user's visual display device. The terminal displays this information, allowing the user to continue working.

[0641] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0642] This invention combines a system that customizes work instructions for each user with an emotion engine that recognizes the user's emotions. The aim of this system is to comprehensively evaluate the user's level of understanding and emotions and provide instructions optimized for the user.

[0643] The system first allows the user (manual creator) to create a standard work instruction document and upload it to the server via a terminal. The server then stores this instruction document in a database, making it accessible to the user (manual user).

[0644] The user refers to the instructions, and if there are any unclear points, they input a question through their terminal and send it to the server. The server analyzes the user's question using a generating AI to determine the user's knowledge level.

[0645] In addition, the emotion engine recognizes the user's emotional state through their words and actions. This information is analyzed from, for example, the phrasing of text, the content of repeated questions, and the typing speed.

[0646] After assessing the user's knowledge level and emotional state, the server uses this data to customize the instructions in a way that best suits the user. Specifically, it may add explanations of technical terms or include more easily understandable examples. Furthermore, if the user is likely to be under stress, the server may add kind words and encouraging messages to the instructions, helping to reduce the user's work pressure.

[0647] Once customized, the instructions are temporarily stored on the server before being sent to the user's terminal. This allows users to refer to instructions tailored to their level of understanding and preferences, enabling them to learn and perform tasks more efficiently.

[0648] For example, if a user is learning how to use new software and the operating procedures are complex and stressful, the system will simplify the instructions and add explanations in user-friendly language. Providing information tailored to the user's situation can improve the efficiency of learning new skills.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The user (manual creator) uses a terminal to create a standard work instruction sheet and uploads it to the server. The server then stores it in its database.

[0652] Step 2:

[0653] The user (manual user) accesses the database via a terminal and views the standard operating instructions. If there are any parts that are difficult to understand while viewing, they can input and send questions from the terminal.

[0654] Step 3:

[0655] The server receives questions from users and sends that data to a generating AI. The generating AI analyzes the content of the questions and evaluates the user's knowledge level.

[0656] Step 4:

[0657] An emotion engine running on the device analyzes the user's input speed, keyboard operation, and expressions to recognize the user's emotional state.

[0658] Step 5:

[0659] The server customizes standard work instructions for the user based on evaluation results from the generating AI and emotional states from the emotion engine. For example, it adds detailed explanations to complex operations and inserts relaxing language if the user is feeling stressed.

[0660] Step 6:

[0661] The server temporarily stores the customized instructions in storage and sends them to the terminal. The terminal displays the instructions to the user, who then continues their work while referring to them.

[0662] Step 7:

[0663] The goal is to ensure that users receive updated instructions and can proceed with their work with confidence, based on information that aligns with their understanding and emotional needs.

[0664] (Example 2)

[0665] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] Conventional instruction management systems often fail to provide instructions in a format that is easy for users to understand and does not take into account their emotional state, which can hinder efficient work execution and learning. This can lead to increased user stress and a decline in the quality of work and learning speed.

[0667] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0668] In this invention, the server includes means for recording standard instructions, means for receiving inquiries from users, and artificial intelligence means for evaluating the user's knowledge level and emotional state. This makes it possible to provide instructions customized according to the user's level of understanding and emotional state.

[0669] A "standard instruction manual" is a document that describes the operating procedures or steps that are generally adopted for a particular task or operation.

[0670] "User" refers to a person who operates the system to create or refer to instruction manuals.

[0671] An "inquiry" refers to information that users use to communicate unclear points or questions that arise when referring to instructions to the server.

[0672] "An artificial intelligence method for evaluating knowledge levels" refers to a technology that analyzes the content of inquiries received from users and measures the specific knowledge and level of understanding that users possess.

[0673] A "means for evaluating emotional state" refers to a technology that analyzes a user's input and actions to determine and evaluate their emotional state.

[0674] "Means of providing customized solutions" refers to the techniques and processes for appropriately adjusting the content of standard instructions based on the user's knowledge level and emotional state, and providing them in the most optimal form for the user.

[0675] This invention is a system for providing standard instructions in a way that is easy for users to understand and that takes their emotional state into consideration. The system is broadly composed of a server, a terminal, and the user.

[0676] The server receives standard instruction manuals from users (instruction manual creators) via their terminals and stores them in appropriate databases. During this process, data management software such as MySQL or MongoDB is used to organize and store metadata. Users (instruction manual users) can access these databases and retrieve the necessary instruction manuals.

[0677] If a user has a question while referring to the instructions, they can input the question through an interface on their terminal and send it to the server. The server receives this inquiry and analyzes the user's knowledge level using a generative AI model. This process utilizes natural language processing tools and models, such as OpenAI's language model.

[0678] Furthermore, the emotion engine evaluates the user's emotional state based on their input. This evaluation uses text analysis technology to infer emotions from factors such as input speed and phrasing patterns. Based on this information, the server appropriately customizes the standard instructions. Specific examples include adding explanations of technical terms and using examples in more user-friendly language. In addition, if the system determines that the user is experiencing high stress levels, it may add encouraging messages.

[0679] Finally, the customized instructions are temporarily stored on the server's storage and sent to the user's terminal. At this point, the terminal displays the instructions on the screen in an appropriate format. The system also uses prompts to optimize the instructions according to the user's state. For example, a prompt might read: "Please provide clearer explanations and simpler examples for the operating procedures that the user finds difficult. Also, please add words of encouragement, as the user may be feeling stressed."

[0680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0681] Step 1:

[0682] The user creates a standard instruction manual and uploads the standard instruction manual file to the server via a terminal. The input is a digital file of the instruction manual. Specifically, the user selects the file through a dedicated interface and presses the send button. The output is a standard instruction manual file saved on the server.

[0683] Step 2:

[0684] The server stores the received standard instructions in a database. The input is a digital file uploaded by the user, and metadata is automatically generated and associated processing is performed when storing it in the database. Specifically, information including the file name, creation date, and creator is tagged. The output is the standard instruction data recorded in the database.

[0685] Step 3:

[0686] If a user has difficulty understanding the instructions, they can enter a question via their terminal and send it to the server. The input is a free-form question text entered by the user. Specifically, the user enters the question into the text box on the terminal and presses the send button. The output at this point is the question text that is sent to the server.

[0687] Step 4:

[0688] The server analyzes received questions using a generative AI model. The input is the user's question text, which is tokenized to extract important keywords. Subsequently, the server evaluates the user's knowledge level based on this. Specifically, the analysis results are scored to determine how specialized the knowledge is. The output is evaluation data indicating the user's knowledge level.

[0689] Step 5:

[0690] The server uses an emotion engine to evaluate the user's emotional state. Inputs include the user's question text and input speed, and the server determines emotions through text analysis. Specifically, it applies algorithms to measure positive and negative emotions and evaluates multiple emotions as needed. The output is data that explicitly indicates the user's emotional state.

[0691] Step 6:

[0692] The server customizes the standard instructions based on the knowledge level and emotional state assessment obtained in the previous step. The inputs are the assessment data and the original standard instructions. Specifically, the server inserts additional explanations and examples into the instructions and modifies the text tone to suit the user. The output is the customized instructions.

[0693] Step 7:

[0694] The server temporarily stores the customized instructions in storage and then sends them to the user's terminal. The input is the customized instruction data, and the output is the instructions displayed on the user's terminal. Specifically, the server verifies the integrity of the data before sending it to the terminal using a communication protocol.

[0695] (Application Example 2)

[0696] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0697] The challenge lies in reducing the stress users experience when using work instructions, particularly those related to understanding and emotions, and thereby improving work efficiency. In particular, in factory and other on-site work environments, there is a lack of instructions tailored to the workers' level of understanding, as well as insufficient emotional support.

[0698] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0699] In this invention, the server includes a device for recording standard work instructions, a device for receiving inquiries from users, an artificial intelligence device for evaluating the user's level of understanding, an emotion recognition device for recognizing and evaluating the user's emotional state, and a device for customizing and providing instructions based on the level of understanding and emotional state. This makes it possible to provide instructions optimized for the user's knowledge level and emotions, thereby reducing the burden on workers and improving work efficiency.

[0700] A "standard work instruction sheet" is a document that describes the procedures and precautions for performing a specific task, and is usually referred to when carrying out work.

[0701] A "device" is a collection of technical components designed to perform a specific function, and includes hardware and software for performing specific operations or processes.

[0702] An "inquiry" refers to a question or request made by a user to obtain unclear points or specific information, and is an action of seeking information from a system.

[0703] "Artificial intelligence" refers to the technology of computer systems that mimic human intellectual behavior and logical reasoning, and specifically to the ability to analyze data and make inferences and judgments.

[0704] "Emotion recognition" is a technology that analyzes and judges a user's emotional state, evaluating the user's mental state from factors such as voice, facial expressions, and behavior.

[0705] "Customization" refers to modifying content to suit the specific needs and circumstances of a particular user, thereby adjusting the content to make it easier for the user to understand and use.

[0706] The system implementing this invention supports users in efficiently utilizing work instructions. The system operates using a terminal that records standard work instructions and receives inquiries from users. The server performs artificial intelligence (AI) processing to analyze user inquiries and thereby evaluate the user's knowledge level. Generative AI models such as OpenAI are used for this purpose.

[0707] Furthermore, by using an emotion recognition device, the system determines the user's emotional state from their facial expressions and tone of voice. For this purpose, for example, Microsoft's Emotion API is utilized. Based on this evaluated information, the server customizes the work instructions in a way that is optimized for the user. This customization includes not only simplifying the terminology in the instructions but also adding encouraging and reassuring messages.

[0708] As a concrete example, consider a scenario where a user uses smart glasses to refer to work instructions. If the user makes a mistake during the task, the glasses detect it and send a query to the server. The server then assesses the user's understanding and emotional state and provides instructions that gently suggest specific next steps. This might be a message like, "You've got the first step down! Now let's try this."

[0709] Examples of prompt statements for a generative AI model are as follows:

[0710] User question: "I don't know where to install this part."

[0711] Prompt message: "The user has accessed the instruction manual page and asked a question about the installation location of a part. The user's knowledge level is intermediate, and they appear slightly stressed. Please provide clear and encouraging instructions."

[0712] This system allows users to receive appropriate guidance based on their level of understanding and emotional needs, enabling them to acquire job skills effectively and efficiently.

[0713] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0714] Step 1:

[0715] The user uses a terminal to refer to work instructions and enters any questions or points of confusion during the process. The entered inquiries are then sent from the terminal to the server.

[0716] Step 2:

[0717] The server analyzes the received inquiry and uses a generative AI model to determine the user's level of understanding. The user's question is used as input, and the user's knowledge level is output as the analysis result.

[0718] Step 3:

[0719] A separate device, an emotion recognition device, detects the user's facial expressions and voice data and analyzes their emotional state. Here, the user's real-time images and voice are used as input, and the user's emotional state is obtained as output.

[0720] Step 4:

[0721] The server integrates the results of the generated AI model and the emotion recognition results to customize the optimal instructions according to the user's level of understanding and emotional state. The input is the aforementioned analysis results, and the output is a customized set of instructions.

[0722] Step 5:

[0723] The customized instructions are temporarily stored on the server. They are then converted to the required format and sent to the terminal.

[0724] Step 6:

[0725] The terminal displays the received customization instructions to the user. The user can then proceed with the work based on the instructions. As a result, the user's work efficiency improves.

[0726] 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.

[0727] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0728] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0729] 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.

[0730] Figure 9 shows an 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.

[0731] 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.

[0732] 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.

[0733] 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, motorcycles, etc., 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, for example, based 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.

[0734] 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."

[0735] 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.

[0736] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0737] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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 the like 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.

[0746] 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.

[0747] The following is further disclosed regarding the embodiments described above.

[0748] (Claim 1)

[0749] Means for recording standard work instructions,

[0750] A means of receiving inquiries from users,

[0751] An artificial intelligence means for evaluating the user's level of understanding based on the aforementioned inquiry,

[0752] A means of customizing and providing the standard work instructions based on the aforementioned evaluation,

[0753] A system that includes this.

[0754] (Claim 2)

[0755] The system according to claim 1, characterized in that it includes means for analyzing the frequency and complexity of terms in the question content when evaluating the user's level of understanding.

[0756] (Claim 3)

[0757] The system according to claim 1, characterized by comprising means for temporarily storing customized instructions and performing preprocessing to provide them to the user.

[0758] "Example 1"

[0759] (Claim 1)

[0760] Means for recording instruction materials,

[0761] A means of receiving inquiries from users,

[0762] An artificial intelligence means for evaluating the user's level of understanding based on the aforementioned inquiry,

[0763] Means for improving and providing the instruction materials based on the above evaluation,

[0764] A means for temporarily storing improved instruction materials and performing preprocessing to provide them to the user,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, characterized in that it includes means for analyzing the frequency and complexity of terms in the question content when evaluating the user's level of understanding.

[0768] (Claim 3)

[0769] The system according to claim 1, characterized by comprising means for analyzing the user's level of understanding using a generative AI model.

[0770] "Application Example 1"

[0771] (Claim 1)

[0772] A device for recording standard work instruction information,

[0773] A device that receives inquiries from users,

[0774] A knowledge processing device that evaluates the user's level of understanding based on the aforementioned inquiry,

[0775] A device that provides the standard work instruction information adapted based on the evaluation,

[0776] A device that provides user-adapted work instruction information in real time via a visual display device,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, characterized in that it includes a device for analyzing the frequency and complexity of terminology used in questions when evaluating the user's level of understanding.

[0780] (Claim 3)

[0781] The system according to claim 1, characterized by comprising a device that intermediately stores adapted work instruction information and performs preliminary processing before providing it to the user.

[0782] "Example 2 of combining an emotion engine"

[0783] (Claim 1)

[0784] Means for recording standard instructions,

[0785] A means of receiving inquiries from users,

[0786] An artificial intelligence means for evaluating the user's knowledge level based on the aforementioned inquiry,

[0787] A means of evaluating the emotional state of users,

[0788] Means for customizing and providing the standard instructions based on the evaluation,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, characterized in that, when evaluating the user's level of understanding, it analyzes the frequency and complexity of terminology used in the inquiry, and in addition, it analyzes the user's emotional state.

[0792] (Claim 3)

[0793] The system according to claim 1, characterized by comprising means for temporarily storing customized instructions and processing them before providing them to a user.

[0794] "Application example 2 when combining with an emotional engine"

[0795] (Claim 1)

[0796] A device for recording standard work instructions,

[0797] A device that receives user inquiries,

[0798] An artificial intelligence device that evaluates the user's level of understanding based on the aforementioned inquiry,

[0799] An emotion recognition device that recognizes and evaluates the user's emotional state,

[0800] A device that customizes and provides the standard work instructions based on the evaluated level of understanding and emotional state,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, characterized in that it includes a device for analyzing the frequency and complexity of terms in the question content when evaluating the user's level of understanding.

[0804] (Claim 3)

[0805] The system according to claim 1, characterized by comprising a device for temporarily storing customized instructions and performing preprocessing to provide them to the user. [Explanation of Symbols]

[0806] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for recording standard work instructions, A means of receiving inquiries from users, An artificial intelligence means for evaluating the user's level of understanding based on the aforementioned inquiry, A means of customizing and providing the standard work instructions based on the aforementioned evaluation, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for analyzing the frequency and complexity of terms in the question content when evaluating the user's level of understanding.

3. The system according to claim 1, characterized by comprising means for temporarily storing customized instructions and performing preprocessing to provide them to the user.

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