system

Abstract

We provide the system. [Solution] A means of analyzing operation history data and converting operation procedures into text, A means of collecting screen display change information and creating a visual guide, A means of analyzing user feedback and updating documents, A means of delivering real-time guidance to operating devices to help users perform operations quickly, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a business system, creating and updating manuals according to operation procedures and system changes requires a huge amount of time and human resources, and is a factor causing human errors. Also, it is difficult to provide an up-to-date manual that immediately corresponds to a changed user interface, and there is also a problem of degrading user usability. Therefore, there is a need for a system that can utilize operation histories and changes in screen displays to generate and update manuals quickly and efficiently.

Means for Solving the Problems

[0005] This invention provides a system that includes means for analyzing operation history data to automatically convert operation procedures into text, means for collecting screen display change information to create visual guides, and means for analyzing user feedback to update manual content as needed. This system enables efficient generation and updating of manuals related to the operation of business systems, facilitating highly accurate user support. This, in turn, reduces working time and improves the quality of manuals.

[0006] "Operation history data" refers to information that shows a record of a series of operations and actions performed by a user on the system.

[0007] "Text conversion" refers to the process of converting collected data or information into a text-based document format.

[0008] "Screen display change information" refers to information about changes to the user interface or screen configuration, and specifies the target and content of those changes.

[0009] "Visual guides" are visual materials such as diagrams, images, and videos used to assist with operating procedures.

[0010] "Feedback" refers to information such as opinions, suggestions, and evaluations provided by users.

[0011] A "manual" is a document that provides information on system operation procedures, settings, troubleshooting, and more. [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] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

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

[0015] In the following embodiments, the numbered processor (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, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

[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 describes a system for automating the generation and updating of manuals based on operations performed by users of a business system. This system mainly consists of three components: a server, a terminal, and a user.

[0034] The server functions as the core of data processing within the system, responsible for collecting, storing, and analyzing user operation history data. Based on this data, the server converts operation procedures into text, analyzes screen display changes, and creates visual guides tailored to various situations. It also utilizes natural language processing and machine learning technologies to update manuals by analyzing user feedback.

[0035] The terminal functions as the user interface, sending changes to the user interface to the server in real time. It is also used as a device to record user actions, for example, by automatically taking screenshots of the screen and sending them to the server. This makes it easy to collect material for providing visual support.

[0036] Users utilize the manuals generated by this system to assist in their daily work. They can proceed with operations based on the information provided by the system, performing tasks while referring to the manuals. Furthermore, users provide feedback as needed, contributing to improving the quality of the manuals.

[0037] As a concrete example, consider the case where a user needs to generate a procedure manual for managing product inventory when a new sales system is introduced. The server collects a history of inventory searches and changes performed by the user and uses that data to create the inventory management manual. The terminal records the screen state as the user interacts with it and sends image data showing the specific button locations and operation flow to the server.

[0038] In this way, the proposed system can improve operational efficiency by efficiently supporting a series of operations within the business system and automatically and continuously providing the latest manuals.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user initiates an operation on the business system and uses a specific function (e.g., product search). The operation history is recorded on the terminal in real time.

[0042] Step 2:

[0043] The terminal sends recorded operation data to the server. This includes clicks, entered data, time information, and more.

[0044] Step 3:

[0045] The server analyzes the received operation history data and converts the operation procedures into text through AI processing. The generated text provides a detailed explanation of those procedures.

[0046] Step 4:

[0047] The device detects changes to the user interface (e.g., the addition of a new button) and sends this information to the server. This information includes the location and characteristics of the changed UI components.

[0048] Step 5:

[0049] Based on UI change information, the server generates new screenshots and videos as needed to create visual guides. These are then integrated into the operation manual.

[0050] Step 6:

[0051] Users view the generated manual and proceed with their tasks. They provide feedback on any difficulties in operation or points they do not understand.

[0052] Step 7:

[0053] The server collects user feedback and analyzes it using natural language processing. Based on the results, it improves the manual, adding details or making corrections where necessary.

[0054] Step 8:

[0055] The server distributes updated manuals throughout the entire system, ensuring that users always have access to the latest information.

[0056] (Example 1)

[0057] 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."

[0058] Traditional methods of creating operational manuals require manual recording and updating of procedures, which is time-consuming and labor-intensive. Furthermore, it is difficult to keep operating procedures and related visual materials up-to-date, resulting in insufficient support for users to perform their tasks accurately and efficiently.

[0059] 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.

[0060] In this invention, the server includes means for analyzing operation history information and converting work procedures into text, means for collecting screen display change information and creating visual guidance, and means for analyzing user feedback and updating documents. This enables automatic and immediate updating of work procedures and the provision of visually integrated support.

[0061] "Operation history information" refers to a record of a series of operations performed by a user on the system.

[0062] A "business procedure" is a description of the steps and sequence of operations necessary to perform a specific task.

[0063] "Text conversion" refers to the process of converting non-text data into textual information and recording it as a written document.

[0064] "Screen display change information" refers to data related to changes and updates that occur on the user interface.

[0065] "Visual guidance" refers to images and illustrated guides used to make operating procedures easier to understand.

[0066] "User feedback" refers to feedback and comments received from people who use the system.

[0067] "Document updating" refers to revising existing documents and manuals based on new information to keep them up-to-date.

[0068] A "terminal" refers to a device used by a user to access and operate a system.

[0069] A "server" refers to a core computing system that processes and manages data.

[0070] "Machine learning technology" is a branch of computer science that automates specific tasks by analyzing large amounts of data and identifying patterns.

[0071] A "generative AI model" refers to a model that uses artificial intelligence to generate new data and information based on a set task.

[0072] "Natural language processing technology" is a technology that uses computers to understand, analyze, and generate human language.

[0073] "Image data" refers to visual information such as screenshots and photographs, which are represented in digital format.

[0074] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user.

[0075] The server forms the core of this system. The server possesses powerful computing capabilities and aggregates and processes operation history information and screen display change information. The server uses natural language processing technology to convert operation history information into text and leverages machine learning technology to identify common operation patterns. Furthermore, the server uses a generative AI model to create visual guidance based on the generated information. The resulting business procedures are then compiled into documents provided to the user. In addition, the server analyzes user feedback and plays a role in continuously updating these documents.

[0076] To give a concrete example, when new software is introduced, the server records the operations performed by the user and analyzes that data to automatically generate an efficient operation manual. For instance, a prompt such as "Please create a manual for registering products using the new inventory management system" could be input into an AI model for generation.

[0077] The terminal is a device that records user actions and transmits that data to the server in real time. The terminal meticulously records user clicks and input actions and takes screenshots of the screen display. This provides the server with image data associated with specific operation steps, improving the quality of visual guidance.

[0078] If user actions are recorded by the terminal, for example, when a user registers new inventory in the system, the procedure is instantly sent to the server. As a result, the user can immediately refer to the new operation manual.

[0079] Users apply the visual guidance and procedural manuals provided by this system to their daily work and provide feedback to the server. When users utilize the new system's functions, they can improve operational efficiency by following the prompts provided by the generated AI model. Furthermore, users can contribute to the overall improvement of the system by providing feedback.

[0080] The implementation of this system will enable rapid information updates and the provision of accurate work procedures, contributing to improved work efficiency for users.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The terminal continuously records the operations performed by the user within the business system. Specifically, it collects logs of buttons clicked and data entered by the user, and automatically takes screenshots of the screen. This information is sent to the server as operation history information and image data.

[0084] Step 2:

[0085] The server receives operation history information sent from the terminal and stores it in a database. Using the input operation history information, the server utilizes natural language processing technology to convert the business procedures into text. This process outputs the specific operations performed by the user in an easy-to-understand format.

[0086] Step 3:

[0087] The server analyzes screen display change information and integrates screenshots obtained using image processing technology as visual guidance. It associates the input image data with operating procedures, enabling the user to visually understand the relevant steps. As a result, the necessary visual information for business procedures is output.

[0088] Step 4:

[0089] The server collects user feedback and analyzes that feedback. Based on the input feedback, it updates existing business procedure documents and incorporates new information and corrections using a generation AI model. This process outputs up-to-date business procedure documents that take into account feedback data from the entire system.

[0090] Step 5:

[0091] Users perform their daily tasks while viewing business procedure documents generated by the server on their terminals. Using prompts provided based on the generated AI model, users can quickly learn and execute new operations. As a result, work efficiency is improved.

[0092] (Application Example 1)

[0093] 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."

[0094] In factory machinery operation, manual manual updates and instruction checks reduce operational efficiency. Furthermore, providing real-time visual guidance is difficult in situations requiring rapid operation, resulting in frequent delays and errors. To overcome these problems and improve operational efficiency, a system is needed that enables automatic generation and updating of operating procedures and real-time instruction delivery.

[0095] 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.

[0096] In this invention, the server includes means for analyzing operation history data and converting operation procedures into text; means for collecting screen display change information and creating visual guides; means for analyzing user feedback and updating documents; and means for delivering real-time guides to operation devices to help users perform operations quickly. This enables improved efficiency and reduced errors in operation tasks.

[0097] "Operation history data" refers to information that records a series of operations performed by a user.

[0098] "Operating instructions" are a set of instructions that show the steps necessary to perform a specific task.

[0099] "Screen display change information" refers to information that indicates how the display state on the user interface has changed.

[0100] A "visual guide" is visual aid information provided to help users understand something.

[0101] "Feedback" refers to opinions and reactions provided by users, which are used to improve the system.

[0102] "Real-time guidance" refers to operational instructions provided to the user at the moment they need them.

[0103] An "operation device" is a device used by a user to receive and execute operational instructions.

[0104] The system for carrying out this invention consists of a server, a terminal, and a user.

[0105] The server, as the central function of the system, collects and analyzes operation history data and transcribes operation procedures into text. This process utilizes natural language processing techniques and machine learning algorithms. Specifically, data analysis and model execution are performed using the Python programming language.

[0106] The server also collects information on screen display changes and generates visual guides based on that information. Based on this information, it also provides real-time guidance to help users complete tasks quickly. User feedback is a critical source of information for improving the system's accuracy.

[0107] On the terminal, user actions are recorded through the interface, and screenshots of the screen are taken and sent to the server. Because this process involves image processing technology, libraries such as OpenCV may be used.

[0108] Users can perform tasks efficiently using the real-time guidance provided through this system. The system offers interactive operational assistance to users via devices such as smart glasses or tablets.

[0109] As a concrete example, when a user replaces a machine part during work, the server generates the latest operating procedure and displays it as a visual guide on smart glasses. At this time, a prompt such as "Generate a detailed visual guide for operators when new machinery is introduced in the factory" is input into the generating AI model. In this way, a system is realized that improves the efficiency of operational tasks and reduces errors.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device detects user actions and records them in real time. Input includes user actions and clicks on the interface. Output is generated operation history data, which is sent to the server. Specifically, the device captures data with adjusted sensitivity and accumulates data when necessary events occur.

[0113] Step 2:

[0114] The server receives and analyzes the operation history from the terminal. The input is operation history data. Based on this data, the server uses natural language processing technology to generate text-based operation procedures. The output is the text-based operation procedures. Specifically, a generation AI model performs annotation and converts the data into text.

[0115] Step 3:

[0116] The server retrieves screen display change information from the terminal and creates a visual guide. The input is a screenshot of the changed screen. The output is image data for the visual guide. The server uses a specific algorithm to visualize the user's actions and other information.

[0117] Step 4:

[0118] Users provide feedback. Users input any feedback into the system via a terminal. The input is feedback data, and the output is recorded by the server to help improve the system. Specifically, the feedback is stored as text data.

[0119] Step 5:

[0120] The server delivers real-time guidance. Inputs are generated operating procedures and visual guides. Outputs are the provision of guidance to the operating device. The server processes the information in real time, generates dedicated prompts, and passes them to the operator.

[0121] This series of processes allows users to easily understand the operating procedures and perform tasks quickly.

[0122] 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.

[0123] This invention combines emotion recognition technology with a system that analyzes user operation history and feedback to provide up-to-date and user-optimized manuals. The system consists of a server, terminals, users, and an emotion engine.

[0124] The server, acting as the central hub for overall data processing, is responsible for collecting and analyzing operation history data and generating manuals. The server analyzes both operation logs and sentiment data received from terminals, correlating user emotions and the actions they performed. This allows for the creation of high-quality manuals that detail operation steps in text and include screen screenshots as visual guides.

[0125] The terminal uses an emotion engine to recognize the user's emotions in real time as they interact with the target system, and sends that data to the server. The terminal also records changes to the user interface and user actions, and transfers that data to the server.

[0126] Users can perform their tasks efficiently through the manuals provided using this system. If a user encounters difficulties during operation or shows an emotional reaction to a particular function, these details are captured by the emotion engine. This allows user experience-based feedback to be fed into the system and used to improve the manuals.

[0127] As a concrete example, consider the introduction of a new project management tool. If a user experiences emotional stress while scheduling tasks, this emotional data is recognized by an emotion engine via the terminal. The server identifies specific steps that caused high stress and improves the manual by providing more detailed explanations of those steps or adding visual guides to make them easier to understand. In this way, an emotion-conscious approach to manuals can increase user satisfaction.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] When a user begins using a specific function of a business system, the terminal monitors the user's actions in real time. Along with the actions, emotion data is acquired by an emotion engine using cameras, microphones, etc.

[0131] Step 2:

[0132] The device sends the acquired operation logs and emotional data to the server. The emotional data includes emotional states (e.g., stress, satisfaction) derived from the analysis of the user's facial expressions and voice.

[0133] Step 3:

[0134] The server analyzes the received data and identifies the operation steps where emotions have changed most significantly. At this stage, natural language processing techniques are used to generate the operation procedure, and explanations that take the emotional data into account are added.

[0135] Step 4:

[0136] The server uses UI information sent from the terminal to create a visual guide, associating screenshots of the screen display with the operation steps. The screenshots are marked to indicate the operation stage where emotional changes occurred.

[0137] Step 5:

[0138] Users utilize the generated manual to continue their work. The manual includes details of areas for improvement and steps that users deem emotionally important.

[0139] Step 6:

[0140] When users perform their tasks based on the generated manuals and provide feedback on parts of the manuals that are difficult to understand or on emotional burdens, that information is also collected through the terminal.

[0141] Step 7:

[0142] The server regularly updates the manual based on sentiment data, including user feedback, and operating procedures, improving it to be more user-friendly before redistributing it.

[0143] (Example 2)

[0144] 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".

[0145] In modern information systems, improving the user experience requires appropriate support and guidance based on user actions. However, traditional manuals often fail to adequately consider user emotions and operational situations, resulting in limited usability. Furthermore, updating documents to quickly incorporate feedback is not easy. Therefore, there is a need to develop flexible, real-time support methods that respond to user actions.

[0146] 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.

[0147] In this invention, the server includes means for collecting operation history data and emotion data and analyzing the user's emotional state, means for transcribing operation procedures into text and creating visual guides, and means for analyzing user feedback and updating the manual. This makes it possible to provide a manual optimized based on the user's individual emotional state in real time.

[0148] "Operation history data" refers to digital information that records a series of actions and procedures related to a user's system operations.

[0149] "Emotional data" refers to digitized information that indicates a user's emotional state, and is obtained based on the analysis of camera and voice data.

[0150] "Means of analysis" refer to the functions and methods used to process collected data, extract meaning from it, and make judgments and decisions.

[0151] "Text conversion" is the process of representing data and operating procedures as textual information.

[0152] "Visual guides" are visual elements such as images, diagrams, and screenshots that present information and operating procedures to users in an easy-to-understand manner.

[0153] "Feedback" refers to information provided by users, such as opinions, reactions, evaluations, and requests regarding operation.

[0154] "Means of updating the manual" refers to methods or functions for modifying or improving the content of the manual based on user feedback and new information.

[0155] "Means of delivery" refers to the functions and methods for presenting the final created manual or guide in a way that is accessible to users.

[0156] The system related to this invention is mainly composed of a server, a terminal, a user, and an emotion engine. The specific operation of each component will be described below.

[0157] The terminal functions as the interface for users to operate the system. It is equipped with a camera and microphone to record operations in real time and recognize user emotions using an emotion engine. Emotion recognition software generally uses algorithms that analyze both audio and video. For example, natural language processing is applied to speech recognition technology, and engines from Microsoft® or APIs from Google® are used.

[0158] The server centrally manages the received data and performs detailed analysis based on it. The server integrates operation history data and sentiment data and performs analysis using an AI model. This AI model uses natural language processing technology to generate text and visual guides that correspond to the user's emotional state. For data analysis, Python's pandas library and database management systems (DBMS) such as MySQL® are used.

[0159] This will give users access to always-updated manuals and guidelines. If a user experiences confusion or stress with a particular operation, that information will be fed back into the system in real time, allowing for more specific and personalized support. For example, if a user finds the instructions for using a new digital tool difficult to understand, that operation will be stored as sentiment data, and the next time they use it, the manual will be provided with more specific explanations.

[0160] A suitable example of a prompt would be, "Analyze which operations users found stressful while using the new tool, and generate suggestions for improving the manual for those operations." In this way, the overall system aims to continuously optimize the user experience in a way that takes emotions into account.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The device collects user actions and corresponding emotional data. When a user clicks the mouse or types on the keyboard, the action history is recorded as a log on the device. Simultaneously, the camera and microphone are used to collect the user's facial expressions and voice tone as emotional data. This information serves as initial input data.

[0164] Step 2:

[0165] The device sends the collected operation history data and sentiment data to the server. During transmission, the data is encrypted using the SSL / TLS protocol to ensure security. At this stage, the transmitted data becomes input to the server.

[0166] Step 3:

[0167] The server stores the received data in a database and begins analysis. It analyzes the operation history data and sentiment data based on an AI model and calculates data correlations. Specifically, it uses the Python pandas library to analyze the emotional state of the user when performing a particular operation. The analysis results become input for the next processing step.

[0168] Step 4:

[0169] The server generates textual instructions and visual guides based on the analysis results. Here, a generative AI model is utilized, and natural language processing techniques are used to create manuals that are tailored to the user's emotions. The resulting manual is the output of this step.

[0170] Step 5:

[0171] The server provides the generated optimized manual to the user via the terminal. By referring to the updated manual, the user can operate the system more smoothly. At this point, the system provides support to further improve the user's experience.

[0172] (Application Example 2)

[0173] 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 device 14 will be referred to as the "terminal."

[0174] In robots used in living spaces, such as home assistive devices, there is a challenge in improving the user experience by providing improved instructional materials for operations that are difficult or stressful for the user. In this situation, conventional methods have the problem of not being able to generate adaptive instructional materials that take the user's emotions into consideration.

[0175] 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.

[0176] In this invention, the server includes means for analyzing operation history data and emotion data and converting operation procedures corresponding to the user's emotions into text; means for collecting screen display change information and creating a visual guide adapted to the user's emotions; means for analyzing user feedback and emotion data and updating and optimizing the document; and means for acquiring emotion data in real time. This makes it possible to provide dynamic and easy-to-understand instructional materials adapted to the user's emotional state.

[0177] "Operation history data" refers to information about the procedures and actions recorded when a user operates the system.

[0178] "Emotional data" refers to information that quantitatively or qualitatively represents a user's psychological state or emotions.

[0179] "A method for transcribing user operation procedures into text that respond to user emotions" refers to a technology that analyzes user operation history and emotion data to express operation procedures in a document that takes emotions into consideration.

[0180] "Screen display change information" refers to information about changes in elements or design on the screen.

[0181] "Means of creating visual guides" refers to techniques for generating designs and elements to visually provide instructions and explanations in a user interface.

[0182] "Feedback analysis" is the process of analyzing opinions and comments provided by users to extract areas for improvement and evaluations.

[0183] "Methods for updating and optimizing documents" refer to technologies for modifying existing documents based on new information and analysis results, making them more effective.

[0184] "Methods for acquiring emotional data in real time" refer to technologies that instantly sense a user's emotional state and collect it as data.

[0185] To implement this invention, three main elements—a server, a terminal, and a user—must function in an integrated manner. The server acts as the central hub for basic data processing, performing analysis using operation history data and sentiment data. Python is used for data analysis and optimization of operation procedures, while OpenCV is utilized for sentiment recognition. Furthermore, cloud services such as AWS® Lambda are used to perform data processing on the server.

[0186] The device is responsible for acquiring emotional data in real time during user interaction and sending it to the server. To achieve this, an emotion engine operates via the device's built-in camera and microphone, analyzing the user's facial expressions and voice tone. An NVIDIA Jetson Nano is used for acquiring emotional data, while a Raspberry Pi is utilized for local data processing and communication.

[0187] When users receive assistance with daily life through consumer robots, they can enjoy an intuitive and comfortable operating experience thanks to optimized operation guides. For example, if a user expresses frustration with the operation of a household assistance robot's cleaning function, the server will suggest more easily understandable operating procedures based on that emotional data.

[0188] As an example of how this system can be applied, if a user expresses a desire to "efficiently complete cleaning for their pet," the system will use emotional data to generate improved guidance. An example of a prompt message might be, "The user expressed frustration with the operation. Please suggest improvements to this operation."

[0189] This technology is a measure to improve usability and enhance the effectiveness of consumer robots by dynamically providing guidance while taking user emotions into consideration.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] When the device begins user interaction, it uses the camera and microphone in real time to sense the user's facial expressions and voice. The collected data is analyzed by an emotion engine and generated as emotion data indicating the user's emotional state. This emotion data is sent to the server along with the operation log. The input is the user's video and audio feeds, and the output is emotion data.

[0193] Step 2:

[0194] The server analyzes operation history data and sentiment data received from the terminal. Using Python, it analyzes this data and associates the user's emotions during operation with the operation steps. In this process, a generative AI model is used to generate prompts and create detailed text-based operation instructions. The input is operation history data and sentiment data, and the output is optimized text-based operation instructions.

[0195] Step 3:

[0196] The server creates a visual guide based on the collected screen display change information. Using OpenCV, it captures screenshots of the screen and associates them with operation steps and emotions, preparing an intuitive and easy-to-understand guide for the user. The input is the collected screen display change information, and the output is the visual guide.

[0197] Step 4:

[0198] The document is updated and further optimized by re-analyzing user feedback and continuously transmitted sentiment data. This provides users with more adaptive instructional materials for subsequent use. The input is user feedback and new sentiment data, and the output is the updated and optimized instructional material.

[0199] Step 5:

[0200] By allowing users to operate consumer robots based on optimized operating procedures and visual guides, an efficient experience becomes possible. A concrete example of user action would be a scenario where the robot cleans according to instructions while simultaneously addressing the user's needs. The output is an improved user experience and appropriate robot operation.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention describes a system for automating the generation and updating of manuals based on operations performed by users of a business system. This system mainly consists of three components: a server, a terminal, and a user.

[0218] The server functions as the core of data processing within the system, responsible for collecting, storing, and analyzing user operation history data. Based on this data, the server converts operation procedures into text, analyzes screen display changes, and creates visual guides tailored to various situations. It also utilizes natural language processing and machine learning technologies to update manuals by analyzing user feedback.

[0219] The terminal functions as the user interface, sending changes to the user interface to the server in real time. It is also used as a device to record user actions, for example, by automatically taking screenshots of the screen and sending them to the server. This makes it easy to collect material for providing visual support.

[0220] Users utilize the manuals generated by this system to assist in their daily work. They can proceed with operations based on the information provided by the system, performing tasks while referring to the manuals. Furthermore, users provide feedback as needed, contributing to improving the quality of the manuals.

[0221] As a concrete example, consider the case where a user needs to generate a procedure manual for managing product inventory when a new sales system is introduced. The server collects a history of inventory searches and changes performed by the user and uses that data to create the inventory management manual. The terminal records the screen state as the user interacts with it and sends image data showing the specific button locations and operation flow to the server.

[0222] In this way, the proposed system can improve operational efficiency by efficiently supporting a series of operations within the business system and automatically and continuously providing the latest manuals.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The user initiates an operation on the business system and uses a specific function (e.g., product search). The operation history is recorded on the terminal in real time.

[0226] Step 2:

[0227] The terminal sends recorded operation data to the server. This includes clicks, entered data, time information, and more.

[0228] Step 3:

[0229] The server analyzes the received operation history data and converts the operation procedures into text through AI processing. The generated text provides a detailed explanation of those procedures.

[0230] Step 4:

[0231] The device detects changes to the user interface (e.g., the addition of a new button) and sends this information to the server. This information includes the location and characteristics of the changed UI components.

[0232] Step 5:

[0233] Based on UI change information, the server generates new screenshots and videos as needed to create visual guides. These are then integrated into the operation manual.

[0234] Step 6:

[0235] Users view the generated manual and proceed with their tasks. They provide feedback on any difficulties in operation or points they do not understand.

[0236] Step 7:

[0237] The server collects user feedback and analyzes it using natural language processing. Based on the results, it improves the manual, adding details or making corrections where necessary.

[0238] Step 8:

[0239] The server distributes updated manuals throughout the entire system, ensuring that users always have access to the latest information.

[0240] (Example 1)

[0241] 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."

[0242] Traditional methods of creating operational manuals require manual recording and updating of procedures, which is time-consuming and labor-intensive. Furthermore, it is difficult to keep operating procedures and related visual materials up-to-date, resulting in insufficient support for users to perform their tasks accurately and efficiently.

[0243] 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.

[0244] In this invention, the server includes means for analyzing operation history information and converting work procedures into text, means for collecting screen display change information and creating visual guidance, and means for analyzing user feedback and updating documents. This enables automatic and immediate updating of work procedures and the provision of visually integrated support.

[0245] "Operation history information" refers to a record of a series of operations performed by a user on the system.

[0246] A "business procedure" is a description of the steps and sequence of operations necessary to perform a specific task.

[0247] "Text conversion" refers to the process of converting non-text data into textual information and recording it as a written document.

[0248] "Screen display change information" refers to data related to changes and updates that occur on the user interface.

[0249] "Visual guidance" refers to images and illustrated guides used to make operating procedures easier to understand.

[0250] "User feedback" refers to feedback and comments received from people who use the system.

[0251] "Document updating" refers to revising existing documents and manuals based on new information to keep them up-to-date.

[0252] A "terminal" refers to a device used by a user to access and operate a system.

[0253] A "server" refers to a core computing system that processes and manages data.

[0254] "Machine learning technology" is a branch of computer science that automates specific tasks by analyzing large amounts of data and identifying patterns.

[0255] A "generative AI model" refers to a model that uses artificial intelligence to generate new data and information based on a set task.

[0256] "Natural language processing technology" is a technology that uses computers to understand, analyze, and generate human language.

[0257] "Image data" refers to visual information such as screenshots and photographs, which are represented in digital format.

[0258] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user.

[0259] The server forms the core of this system. The server possesses powerful computing capabilities and aggregates and processes operation history information and screen display change information. The server uses natural language processing technology to convert operation history information into text and leverages machine learning technology to identify common operation patterns. Furthermore, the server uses a generative AI model to create visual guidance based on the generated information. The resulting business procedures are then compiled into documents provided to the user. In addition, the server analyzes user feedback and plays a role in continuously updating these documents.

[0260] To give a concrete example, when new software is introduced, the server records the operations performed by the user and analyzes that data to automatically generate an efficient operation manual. For instance, a prompt such as "Please create a manual for registering products using the new inventory management system" could be input into an AI model for generation.

[0261] The terminal is a device that records user actions and transmits that data to the server in real time. The terminal meticulously records user clicks and input actions and takes screenshots of the screen display. This provides the server with image data associated with specific operation steps, improving the quality of visual guidance.

[0262] If user actions are recorded by the terminal, for example, when a user registers new inventory in the system, the procedure is instantly sent to the server. As a result, the user can immediately refer to the new operation manual.

[0263] Users apply the visual guidance and procedural manuals provided by this system to their daily work and provide feedback to the server. When users utilize the new system's functions, they can improve operational efficiency by following the prompts provided by the generated AI model. Furthermore, users can contribute to the overall improvement of the system by providing feedback.

[0264] The implementation of this system will enable rapid information updates and the provision of accurate work procedures, contributing to improved work efficiency for users.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The terminal continuously records the operations performed by the user on the business system. Specifically, it collects logs of buttons clicked and data entered by the user, and automatically takes screenshots of the screen. This information is sent to the server as operation history information and image data.

[0268] Step 2:

[0269] The server receives operation history information sent from the terminal and stores it in a database. Using the input operation history information, the server utilizes natural language processing technology to convert the business procedures into text. This process outputs the specific operations performed by the user in an easy-to-understand format.

[0270] Step 3:

[0271] The server analyzes screen display change information and integrates screenshots obtained using image processing technology as visual guidance. It associates the input image data with operating procedures, enabling the user to visually understand the relevant steps. As a result, the necessary visual information for business procedures is output.

[0272] Step 4:

[0273] The server collects user feedback and analyzes that feedback. Based on the input feedback, it updates existing business procedure documents and incorporates new information and corrections using a generation AI model. This process outputs up-to-date business procedure documents that take into account feedback data from the entire system.

[0274] Step 5:

[0275] Users perform their daily tasks while viewing business procedure documents generated by the server on their terminals. Using prompts provided based on the generated AI model, users can quickly learn and execute new operations. As a result, work efficiency is improved.

[0276] (Application Example 1)

[0277] 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."

[0278] In factory machinery operation, manual manual updates and instruction checks reduce operational efficiency. Furthermore, providing real-time visual guidance is difficult in situations requiring rapid operation, resulting in frequent delays and errors. To overcome these problems and improve operational efficiency, a system is needed that enables automatic generation and updating of operating procedures and real-time instruction delivery.

[0279] 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.

[0280] In this invention, the server includes means for analyzing operation history data and converting operation procedures into text; means for collecting screen display change information and creating visual guides; means for analyzing user feedback and updating documents; and means for delivering real-time guides to operation devices to help users perform operations quickly. This enables improved efficiency and reduced errors in operation tasks.

[0281] "Operation history data" refers to information that records a series of operations performed by a user.

[0282] An "operation procedure" is an instruction indicating a series of steps necessary to execute a specific task.

[0283] "Screen display change information" is information indicating how the display state on the user interface has changed.

[0284] A "visual guide" is visual support information provided to assist the user's understanding.

[0285] "Feedback" refers to opinions and reactions provided by the user and is used to improve the system.

[0286] A "real-time guide" is an operation instruction provided at the moment the user needs it.

[0287] An "operation device" is a device used by the user to receive or execute operation instructions.

[0288] The system for implementing this invention consists of a server, a terminal, and a user.

[0289] The server, as the central function of the system, collects, analyzes operation history data, and texturizes operation procedures. Natural language processing technology and machine learning algorithms are used in this process. Specifically, data analysis and model execution are performed using the Python programming language.

[0290] In addition, the server collects screen display change information and generates visual guides based on it. Based on this information, real-time guides are also provided so that the user can quickly perform tasks. Feedback from the user becomes a critical information source for improving the accuracy of the system.

[0291] On the terminal, user actions are recorded through the interface, and screenshots of the screen are taken and sent to the server. Because this process involves image processing technology, libraries such as OpenCV may be used.

[0292] Users can perform tasks efficiently using the real-time guidance provided through this system. The system offers interactive operational assistance to users via devices such as smart glasses or tablets.

[0293] As a concrete example, when a user replaces a machine part during work, the server generates the latest operating procedure and displays it as a visual guide on smart glasses. At this time, a prompt such as "Generate a detailed visual guide for operators when new machinery is introduced in the factory" is input into the generating AI model. In this way, a system is realized that improves the efficiency of operational tasks and reduces errors.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The device detects user actions and records them in real time. Input includes user actions and clicks on the interface. Output is generated operation history data, which is sent to the server. Specifically, the device captures data with adjusted sensitivity and accumulates data when necessary events occur.

[0297] Step 2:

[0298] The server receives and analyzes the operation history from the terminal. The input is operation history data. Based on this data, the server uses natural language processing technology to generate text-based operation procedures. The output is the text-based operation procedures. Specifically, a generation AI model performs annotation and converts the data into text.

[0299] Step 3:

[0300] The server retrieves screen display change information from the terminal and creates a visual guide. The input is a screenshot of the changed screen. The output is image data for the visual guide. The server uses a specific algorithm to visualize the user's actions and other information.

[0301] Step 4:

[0302] Users provide feedback. Users input any feedback into the system via a terminal. The input is feedback data, and the output is recorded by the server to help improve the system. Specifically, the feedback is stored as text data.

[0303] Step 5:

[0304] The server delivers real-time guidance. Inputs are generated operating procedures and visual guides. Outputs are the provision of guidance to the operating device. The server processes the information in real time, generates dedicated prompts, and passes them to the operator.

[0305] This series of processes allows users to easily understand the operating procedures and perform tasks quickly.

[0306] 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.

[0307] This invention combines emotion recognition technology with a system that analyzes user operation history and feedback to provide up-to-date and user-optimized manuals. The system consists of a server, terminals, users, and an emotion engine.

[0308] As the center of overall data processing, the server is responsible for collecting, analyzing operation history data, and generating manuals. The server analyzes both the operation logs and emotion data received from the terminal, associating how the user performed an operation with what emotion. This enables the detailed textification of operation steps and the generation of high-quality manuals that include screen shots of the screen as visual guides.

[0309] When the user operates the target system, the terminal uses an emotion engine to recognize the user's emotion in real time and transmits the data to the server. The terminal also has the role of recording changes to the user interface and the user's operations and transferring them to the server.

[0310] Through the manuals provided using this system, the user can perform their work efficiently. When the user stumbles in an operation or shows an emotional reaction regarding a specific function, the details are captured by the emotion engine. This feeds back user experience-based feedback to the system, which is utilized to improve the manual.

[0311] As a specific example, consider the introduction of a new project management tool. If the user feels emotional stress in setting the schedule of tasks, the emotion data is recognized by the emotion engine through the terminal. The server identifies the specific operation procedure with increased stress and improves the manual by explaining that procedure in more detail or adding a visual guide to make it more understandable. In this way, the manual approach considering emotions can enhance user satisfaction.

[0312] The following explains the processing flow.

[0313] Step 1:

[0314] When a user begins using a specific function of a business system, the terminal monitors the user's actions in real time. Along with the actions, emotion data is acquired by an emotion engine using cameras, microphones, etc.

[0315] Step 2:

[0316] The device sends the acquired operation logs and emotional data to the server. The emotional data includes emotional states (e.g., stress, satisfaction) derived from the analysis of the user's facial expressions and voice.

[0317] Step 3:

[0318] The server analyzes the received data and identifies the operation steps where emotions have changed most significantly. At this stage, natural language processing techniques are used to generate the operation procedure, and explanations that take the emotional data into account are added.

[0319] Step 4:

[0320] The server uses UI information sent from the terminal to create a visual guide, associating screenshots of the screen display with the operation steps. The screenshots are marked to indicate the operation stage where emotional changes occurred.

[0321] Step 5:

[0322] Users utilize the generated manual to continue their work. The manual includes details of areas for improvement and steps that users deem emotionally important.

[0323] Step 6:

[0324] When users perform their tasks based on the generated manuals and provide feedback on parts of the manuals that are difficult to understand or on emotional burdens, that information is also collected through the terminal.

[0325] Step 7:

[0326] The server regularly updates the manual based on sentiment data, including user feedback, and operating procedures, improving it to be more user-friendly before redistributing it.

[0327] (Example 2)

[0328] 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 glasses 214 will be referred to as the "terminal".

[0329] In modern information systems, improving the user experience requires appropriate support and guidance based on user actions. However, traditional manuals often fail to adequately consider user emotions and operational situations, resulting in limited usability. Furthermore, updating documents to quickly incorporate feedback is not easy. Therefore, there is a need to develop flexible, real-time support methods that respond to user actions.

[0330] 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.

[0331] In this invention, the server includes means for collecting operation history data and emotion data and analyzing the user's emotional state, means for transcribing operation procedures into text and creating visual guides, and means for analyzing user feedback and updating the manual. This makes it possible to provide a manual optimized based on the user's individual emotional state in real time.

[0332] "Operation history data" refers to digital information that records a series of actions and procedures related to a user's system operations.

[0333] "Emotional data" refers to digitized information that indicates a user's emotional state, and is obtained based on the analysis of camera and voice data.

[0334] "Means of analysis" refer to the functions and methods used to process collected data, extract meaning from it, and make judgments and decisions.

[0335] "Text conversion" is the process of representing data and operating procedures as textual information.

[0336] "Visual guides" are visual elements such as images, diagrams, and screenshots that present information and operating procedures to users in an easy-to-understand manner.

[0337] "Feedback" refers to information provided by users, such as opinions, reactions, evaluations, and requests regarding operation.

[0338] "Means of updating the manual" refers to methods or functions for modifying or improving the content of the manual based on user feedback and new information.

[0339] "Means of delivery" refers to the functions and methods for presenting the final created manual or guide in a way that is accessible to users.

[0340] The system related to this invention is mainly composed of a server, a terminal, a user, and an emotion engine. The specific operation of each component will be described below.

[0341] The terminal functions as the interface for users to operate the system. It is equipped with a camera and microphone to record operations in real time and recognize user emotions using an emotion engine. Emotion recognition software generally uses algorithms that analyze both audio and video. For example, natural language processing is applied to speech recognition technology, and engines from Microsoft or APIs from Google are used.

[0342] The server centrally manages the received data and performs detailed analysis based on it. The server integrates operation history data and sentiment data and performs analysis using an AI model. This AI model uses natural language processing technology to generate text and visual guides that correspond to the user's emotional state. The data analysis uses Python's pandas library and a database management system (DBMS) such as MySQL.

[0343] This will give users access to always-updated manuals and guidelines. If a user experiences confusion or stress with a particular operation, that information will be fed back into the system in real time, allowing for more specific and personalized support. For example, if a user finds the instructions for using a new digital tool difficult to understand, that operation will be stored as sentiment data, and the next time they use it, the manual will be provided with more specific explanations.

[0344] A suitable example of a prompt would be, "Analyze which operations users found stressful while using the new tool, and generate suggestions for improving the manual for those operations." In this way, the overall system aims to continuously optimize the user experience in a way that takes emotions into account.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The device collects user actions and corresponding emotional data. When a user clicks the mouse or types on the keyboard, the action history is recorded as a log on the device. Simultaneously, the camera and microphone are used to collect the user's facial expressions and voice tone as emotional data. This information serves as initial input data.

[0348] Step 2:

[0349] The device sends the collected operation history data and sentiment data to the server. During transmission, the data is encrypted using the SSL / TLS protocol to ensure security. At this stage, the transmitted data becomes input to the server.

[0350] Step 3:

[0351] The server stores the received data in a database and begins analysis. It analyzes the operation history data and sentiment data based on an AI model and calculates data correlations. Specifically, it uses the Python pandas library to analyze the emotional state of the user when performing a particular operation. The analysis results become input for the next processing step.

[0352] Step 4:

[0353] The server generates textual instructions and visual guides based on the analysis results. Here, a generative AI model is utilized, and natural language processing techniques are used to create manuals that are tailored to the user's emotions. The resulting manual is the output of this step.

[0354] Step 5:

[0355] The server provides the generated optimized manual to the user via the terminal. By referring to the updated manual, the user can operate the system more smoothly. At this point, the system provides support to further improve the user's experience.

[0356] (Application Example 2)

[0357] 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 as the "terminal".

[0358] In robots used in living spaces, such as home assistive devices, there is a challenge in improving the user experience by providing improved instructional materials for operations that are difficult or stressful for the user. In this situation, conventional methods have the problem of not being able to generate adaptive instructional materials that take the user's emotions into consideration.

[0359] 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.

[0360] In this invention, the server includes means for analyzing operation history data and emotion data and converting operation procedures corresponding to the user's emotions into text; means for collecting screen display change information and creating a visual guide adapted to the user's emotions; means for analyzing user feedback and emotion data and updating and optimizing the document; and means for acquiring emotion data in real time. This makes it possible to provide dynamic and easy-to-understand instructional materials adapted to the user's emotional state.

[0361] "Operation history data" refers to information about the procedures and actions recorded when a user operates the system.

[0362] "Emotional data" refers to information that quantitatively or qualitatively represents a user's psychological state or emotions.

[0363] "A method for transcribing user operation procedures into text that respond to user emotions" refers to a technology that analyzes user operation history and emotion data to express operation procedures in a document that takes emotions into consideration.

[0364] "Screen display change information" refers to information about changes in elements or design on the screen.

[0365] "Means of creating visual guides" refers to techniques for generating designs and elements to visually provide instructions and explanations in a user interface.

[0366] "Feedback analysis" is the process of analyzing opinions and comments provided by users to extract areas for improvement and evaluations.

[0367] "Methods for updating and optimizing documents" refer to technologies for modifying existing documents based on new information and analysis results, making them more effective.

[0368] "Methods for acquiring emotional data in real time" refer to technologies that instantly sense a user's emotional state and collect it as data.

[0369] To implement this invention, three main elements—a server, a terminal, and a user—must function in an integrated manner. The server acts as the central hub for basic data processing, performing analysis using operation history data and sentiment data. Python is used for data analysis and optimization of operation procedures, while OpenCV is utilized for sentiment recognition. Furthermore, cloud services such as AWS Lambda are used to perform data processing on the server.

[0370] The device is responsible for acquiring emotional data in real time during user interaction and sending it to the server. To achieve this, an emotion engine operates via the device's built-in camera and microphone, analyzing the user's facial expressions and voice tone. An NVIDIA Jetson Nano is used for acquiring emotional data, while a Raspberry Pi is utilized for local data processing and communication.

[0371] When users receive assistance with daily life through consumer robots, they can enjoy an intuitive and comfortable operating experience thanks to optimized operation guides. For example, if a user expresses frustration with the operation of a household assistance robot's cleaning function, the server will suggest more easily understandable operating procedures based on that emotional data.

[0372] As an example of how this system can be applied, if a user expresses a desire to "efficiently complete cleaning for their pet," the system will use emotional data to generate improved guidance. An example of a prompt message might be, "The user expressed frustration with the operation. Please suggest improvements to this operation."

[0373] This technology is a measure to improve usability and enhance the effectiveness of consumer robots by dynamically providing guidance while taking user emotions into consideration.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] When the device begins user interaction, it uses the camera and microphone in real time to sense the user's facial expressions and voice. The collected data is analyzed by an emotion engine and generated as emotion data indicating the user's emotional state. This emotion data is sent to the server along with the operation log. The input is the user's video and audio feeds, and the output is emotion data.

[0377] Step 2:

[0378] The server analyzes operation history data and sentiment data received from the terminal. Using Python, it analyzes this data and associates the user's emotions during operation with the operation steps. In this process, a generative AI model is used to generate prompts and create detailed text-based operation instructions. The input is operation history data and sentiment data, and the output is optimized text-based operation instructions.

[0379] Step 3:

[0380] The server creates a visual guide based on the collected screen display change information. Using OpenCV, it captures screenshots of the screen and associates them with operation steps and emotions, preparing an intuitive and easy-to-understand guide for the user. The input is the collected screen display change information, and the output is the visual guide.

[0381] Step 4:

[0382] The document is updated and further optimized by re-analyzing user feedback and continuously transmitted sentiment data. This provides users with more adaptive instructional materials for subsequent use. The input is user feedback and new sentiment data, and the output is the updated and optimized instructional material.

[0383] Step 5:

[0384] By allowing users to operate consumer robots based on optimized operating procedures and visual guides, an efficient experience becomes possible. A concrete example of user action would be a scenario where the robot cleans according to instructions while simultaneously addressing the user's needs. The output is an improved user experience and appropriate robot operation.

[0385] 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.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention describes a system for automating the generation and updating of manuals based on operations performed by users of a business system. This system mainly consists of three components: a server, a terminal, and a user.

[0402] The server functions as the core of data processing within the system, responsible for collecting, storing, and analyzing user operation history data. Based on this data, the server converts operation procedures into text, analyzes screen display changes, and creates visual guides tailored to various situations. It also utilizes natural language processing and machine learning technologies to update manuals by analyzing user feedback.

[0403] The terminal functions as the user interface, sending changes to the user interface to the server in real time. It is also used as a device to record user actions, for example, by automatically taking screenshots of the screen and sending them to the server. This makes it easy to collect material for providing visual support.

[0404] Users utilize the manuals generated by this system to assist in their daily work. They can proceed with operations based on the information provided by the system, performing tasks while referring to the manuals. Furthermore, users provide feedback as needed, contributing to improving the quality of the manuals.

[0405] As a concrete example, consider the case where a user needs to generate a procedure manual for managing product inventory when a new sales system is introduced. The server collects a history of inventory searches and changes performed by the user and uses that data to create the inventory management manual. The terminal records the screen state as the user interacts with it and sends image data showing the specific button locations and operation flow to the server.

[0406] In this way, the proposed system can improve operational efficiency by efficiently supporting a series of operations within the business system and automatically and continuously providing the latest manuals.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The user initiates an operation on the business system and uses a specific function (e.g., product search). The operation history is recorded on the terminal in real time.

[0410] Step 2:

[0411] The terminal sends recorded operation data to the server. This includes clicks, entered data, time information, and more.

[0412] Step 3:

[0413] The server analyzes the received operation history data and converts the operation procedures into text through AI processing. The generated text provides a detailed explanation of those procedures.

[0414] Step 4:

[0415] The device detects changes to the user interface (e.g., the addition of a new button) and sends this information to the server. This information includes the location and characteristics of the changed UI components.

[0416] Step 5:

[0417] Based on UI change information, the server generates new screenshots and videos as needed to create visual guides. These are then integrated into the operation manual.

[0418] Step 6:

[0419] Users view the generated manual and proceed with their tasks. They provide feedback on any difficulties in operation or points they do not understand.

[0420] Step 7:

[0421] The server collects user feedback and analyzes it using natural language processing. Based on the results, it improves the manual, adding details or making corrections where necessary.

[0422] Step 8:

[0423] The server distributes updated manuals throughout the entire system, ensuring that users always have access to the latest information.

[0424] (Example 1)

[0425] 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."

[0426] Traditional methods of creating operational manuals require manual recording and updating of procedures, which is time-consuming and labor-intensive. Furthermore, it is difficult to keep operating procedures and related visual materials up-to-date, resulting in insufficient support for users to perform their tasks accurately and efficiently.

[0427] 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.

[0428] In this invention, the server includes means for analyzing operation history information and converting work procedures into text, means for collecting screen display change information and creating visual guidance, and means for analyzing user feedback and updating documents. This enables automatic and immediate updating of work procedures and the provision of visually integrated support.

[0429] "Operation history information" refers to a record of a series of operations performed by a user on the system.

[0430] A "business procedure" is a description of the steps and sequence of operations necessary to perform a specific task.

[0431] "Text conversion" refers to the process of converting non-text data into textual information and recording it as a written document.

[0432] "Screen display change information" refers to data related to changes and updates that occur on the user interface.

[0433] "Visual guidance" refers to images and illustrated guides used to make operating procedures easier to understand.

[0434] "User feedback" refers to feedback and comments received from people who use the system.

[0435] "Document updating" refers to revising existing documents and manuals based on new information to keep them up-to-date.

[0436] A "terminal" refers to a device used by a user to access and operate a system.

[0437] A "server" refers to a core computing system that processes and manages data.

[0438] "Machine learning technology" is a branch of computer science that automates specific tasks by analyzing large amounts of data and identifying patterns.

[0439] A "generative AI model" refers to a model that uses artificial intelligence to generate new data and information based on a set task.

[0440] "Natural language processing technology" is a technology that uses computers to understand, analyze, and generate human language.

[0441] "Image data" refers to visual information such as screenshots and photographs, which are represented in digital format.

[0442] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user.

[0443] The server forms the core of this system. The server possesses powerful computing capabilities and aggregates and processes operation history information and screen display change information. The server uses natural language processing technology to convert operation history information into text and leverages machine learning technology to identify common operation patterns. Furthermore, the server uses a generative AI model to create visual guidance based on the generated information. The resulting business procedures are then compiled into documents provided to the user. In addition, the server analyzes user feedback and plays a role in continuously updating these documents.

[0444] To give a concrete example, when new software is introduced, the server records the operations performed by the user and analyzes that data to automatically generate an efficient operation manual. For instance, a prompt such as "Please create a manual for registering products using the new inventory management system" could be input into an AI model for generation.

[0445] The terminal is a device that records user actions and transmits that data to the server in real time. The terminal meticulously records user clicks and input actions and takes screenshots of the screen display. This provides the server with image data associated with specific operation steps, improving the quality of visual guidance.

[0446] If user actions are recorded by the terminal, for example, when a user registers new inventory in the system, the procedure is instantly sent to the server. As a result, the user can immediately refer to the new operation manual.

[0447] Users apply the visual guidance and procedural manuals provided by this system to their daily work and provide feedback to the server. When users utilize the new system's functions, they can improve operational efficiency by following the prompts provided by the generated AI model. Furthermore, users can contribute to the overall improvement of the system by providing feedback.

[0448] The implementation of this system will enable rapid information updates and the provision of accurate work procedures, contributing to improved work efficiency for users.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The terminal continuously records the operations performed by the user on the business system. Specifically, it collects logs of buttons clicked and data entered by the user, and automatically takes screenshots of the screen. This information is sent to the server as operation history information and image data.

[0452] Step 2:

[0453] The server receives operation history information sent from the terminal and stores it in a database. Using the input operation history information, the server utilizes natural language processing technology to convert the business procedures into text. This process outputs the specific operations performed by the user in an easy-to-understand format.

[0454] Step 3:

[0455] The server analyzes screen display change information and integrates screenshots obtained using image processing technology as visual guidance. It associates the input image data with operating procedures, enabling the user to visually understand the relevant steps. As a result, the necessary visual information for business procedures is output.

[0456] Step 4:

[0457] The server collects user feedback and analyzes that feedback. Based on the input feedback, it updates existing business procedure documents and incorporates new information and corrections using a generation AI model. This process outputs up-to-date business procedure documents that take into account feedback data from the entire system.

[0458] Step 5:

[0459] Users perform their daily tasks while viewing business procedure documents generated by the server on their terminals. Using prompts provided based on the generated AI model, users can quickly learn and execute new operations. As a result, work efficiency is improved.

[0460] (Application Example 1)

[0461] 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."

[0462] In factory machinery operation, manual manual updates and instruction checks reduce operational efficiency. Furthermore, providing real-time visual guidance is difficult in situations requiring rapid operation, resulting in frequent delays and errors. To overcome these problems and improve operational efficiency, a system is needed that enables automatic generation and updating of operating procedures and real-time instruction delivery.

[0463] 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.

[0464] In this invention, the server includes means for analyzing operation history data and converting operation procedures into text; means for collecting screen display change information and creating visual guides; means for analyzing user feedback and updating documents; and means for delivering real-time guides to operation devices to help users perform operations quickly. This enables improved efficiency and reduced errors in operation tasks.

[0465] "Operation history data" refers to information that records a series of operations performed by a user.

[0466] "Operating instructions" are a set of instructions that show the steps necessary to perform a specific task.

[0467] "Screen display change information" refers to information that indicates how the display state on the user interface has changed.

[0468] A "visual guide" is visual aid information provided to help users understand something.

[0469] "Feedback" refers to opinions and reactions provided by users, which are used to improve the system.

[0470] "Real-time guidance" refers to operational instructions provided to the user at the moment they need them.

[0471] An "operation device" is a device used by a user to receive and execute operational instructions.

[0472] The system for carrying out this invention consists of a server, a terminal, and a user.

[0473] The server, as the central function of the system, collects and analyzes operation history data and transcribes operation procedures into text. This process utilizes natural language processing techniques and machine learning algorithms. Specifically, data analysis and model execution are performed using the Python programming language.

[0474] The server also collects information on screen display changes and generates visual guides based on that information. Based on this information, it also provides real-time guidance to help users complete tasks quickly. User feedback is a critical source of information for improving the system's accuracy.

[0475] On the terminal, user actions are recorded through the interface, and screenshots of the screen are taken and sent to the server. Because this process involves image processing technology, libraries such as OpenCV may be used.

[0476] Users can perform tasks efficiently using the real-time guidance provided through this system. The system offers interactive operational assistance to users via devices such as smart glasses or tablets.

[0477] As a concrete example, when a user replaces a machine part during work, the server generates the latest operating procedure and displays it as a visual guide on smart glasses. At this time, a prompt such as "Generate a detailed visual guide for operators when new machinery is introduced in the factory" is input into the generating AI model. In this way, a system is realized that improves the efficiency of operational tasks and reduces errors.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The device detects user actions and records them in real time. Input includes user actions and clicks on the interface. Output is generated operation history data, which is sent to the server. Specifically, the device captures data with adjusted sensitivity and accumulates data when necessary events occur.

[0481] Step 2:

[0482] The server receives and analyzes the operation history from the terminal. The input is operation history data. Based on this data, the server uses natural language processing technology to generate text-based operation procedures. The output is the text-based operation procedures. Specifically, a generation AI model performs annotation and converts the data into text.

[0483] Step 3:

[0484] The server retrieves screen display change information from the terminal and creates a visual guide. The input is a screenshot of the changed screen. The output is image data for the visual guide. The server uses a specific algorithm to visualize the user's actions and other information.

[0485] Step 4:

[0486] Users provide feedback. Users input any feedback into the system via a terminal. The input is feedback data, and the output is recorded by the server to help improve the system. Specifically, the feedback is stored as text data.

[0487] Step 5:

[0488] The server delivers real-time guidance. Inputs are generated operating procedures and visual guides. Outputs are the provision of guidance to the operating device. The server processes the information in real time, generates dedicated prompts, and passes them to the operator.

[0489] This series of processes allows users to easily understand the operating procedures and perform tasks quickly.

[0490] 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.

[0491] This invention combines emotion recognition technology with a system that analyzes user operation history and feedback to provide up-to-date and user-optimized manuals. The system consists of a server, terminals, users, and an emotion engine.

[0492] The server, acting as the central hub for overall data processing, is responsible for collecting and analyzing operation history data and generating manuals. The server analyzes both operation logs and sentiment data received from terminals, correlating user emotions and the actions they performed. This allows for the creation of high-quality manuals that detail operation steps in text and include screen screenshots as visual guides.

[0493] The terminal uses an emotion engine to recognize the user's emotions in real time as they interact with the target system, and sends that data to the server. The terminal also records changes to the user interface and user actions, and transfers that data to the server.

[0494] Users can perform their tasks efficiently through the manuals provided using this system. If a user encounters difficulties during operation or shows an emotional reaction to a particular function, these details are captured by the emotion engine. This allows user experience-based feedback to be fed into the system and used to improve the manuals.

[0495] As a concrete example, consider the introduction of a new project management tool. If a user experiences emotional stress while scheduling tasks, this emotional data is recognized by an emotion engine via the terminal. The server identifies specific steps that caused high stress and improves the manual by providing more detailed explanations of those steps or adding visual guides to make them easier to understand. In this way, an emotion-conscious approach to manuals can increase user satisfaction.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] When a user begins using a specific function of a business system, the terminal monitors the user's actions in real time. Along with the actions, emotion data is acquired by an emotion engine using cameras, microphones, etc.

[0499] Step 2:

[0500] The device sends the acquired operation logs and emotional data to the server. The emotional data includes emotional states (e.g., stress, satisfaction) derived from the analysis of the user's facial expressions and voice.

[0501] Step 3:

[0502] The server analyzes the received data and identifies the operation steps where emotions have changed most significantly. At this stage, natural language processing techniques are used to generate the operation procedure, and explanations that take the emotional data into account are added.

[0503] Step 4:

[0504] The server uses UI information sent from the terminal to create a visual guide, associating screenshots of the screen display with the operation steps. The screenshots are marked to indicate the operation stage where emotional changes occurred.

[0505] Step 5:

[0506] Users utilize the generated manual to continue their work. The manual includes details of areas for improvement and steps that users deem emotionally important.

[0507] Step 6:

[0508] When users perform their tasks based on the generated manuals and provide feedback on parts of the manuals that are difficult to understand or on emotional burdens, that information is also collected through the terminal.

[0509] Step 7:

[0510] The server regularly updates the manual based on sentiment data, including user feedback, and operating procedures, improving it to be more user-friendly before redistributing it.

[0511] (Example 2)

[0512] 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."

[0513] In modern information systems, improving the user experience requires appropriate support and guidance based on user actions. However, traditional manuals often fail to adequately consider user emotions and operational situations, resulting in limited usability. Furthermore, updating documents to quickly incorporate feedback is not easy. Therefore, there is a need to develop flexible, real-time support methods that respond to user actions.

[0514] 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.

[0515] In this invention, the server includes means for collecting operation history data and emotion data and analyzing the user's emotional state, means for transcribing operation procedures into text and creating visual guides, and means for analyzing user feedback and updating the manual. This makes it possible to provide a manual optimized based on the user's individual emotional state in real time.

[0516] "Operation history data" refers to digital information that records a series of actions and procedures related to a user's system operations.

[0517] "Emotional data" refers to digitized information that indicates a user's emotional state, and is obtained based on the analysis of camera and voice data.

[0518] "Means of analysis" refer to the functions and methods used to process collected data, extract meaning from it, and make judgments and decisions.

[0519] "Text conversion" is the process of representing data and operating procedures as textual information.

[0520] "Visual guides" are visual elements such as images, diagrams, and screenshots that present information and operating procedures to users in an easy-to-understand manner.

[0521] "Feedback" refers to information provided by users, such as opinions, reactions, evaluations, and requests regarding operation.

[0522] "Means of updating the manual" refers to methods or functions for modifying or improving the content of the manual based on user feedback and new information.

[0523] "Means of delivery" refers to the functions and methods for presenting the final created manual or guide in a way that is accessible to users.

[0524] The system related to this invention is mainly composed of a server, a terminal, a user, and an emotion engine. The specific operation of each component will be described below.

[0525] The terminal functions as the interface for users to operate the system. It is equipped with a camera and microphone to record operations in real time and recognize user emotions using an emotion engine. Emotion recognition software generally uses algorithms that analyze both audio and video. For example, natural language processing is applied to speech recognition technology, and engines from Microsoft or APIs from Google are used.

[0526] The server centrally manages the received data and performs detailed analysis based on it. The server integrates operation history data and sentiment data and performs analysis using an AI model. This AI model uses natural language processing technology to generate text and visual guides that correspond to the user's emotional state. The data analysis uses Python's pandas library and a database management system (DBMS) such as MySQL.

[0527] This will give users access to always-updated manuals and guidelines. If a user experiences confusion or stress with a particular operation, that information will be fed back into the system in real time, allowing for more specific and personalized support. For example, if a user finds the instructions for using a new digital tool difficult to understand, that operation will be stored as sentiment data, and the next time they use it, the manual will be provided with more specific explanations.

[0528] A suitable example of a prompt would be, "Analyze which operations users found stressful while using the new tool, and generate suggestions for improving the manual for those operations." In this way, the overall system aims to continuously optimize the user experience in a way that takes emotions into account.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The device collects user actions and corresponding emotional data. When a user clicks the mouse or types on the keyboard, the action history is recorded as a log on the device. Simultaneously, the camera and microphone are used to collect the user's facial expressions and voice tone as emotional data. This information serves as initial input data.

[0532] Step 2:

[0533] The device sends the collected operation history data and sentiment data to the server. During transmission, the data is encrypted using the SSL / TLS protocol to ensure security. At this stage, the transmitted data becomes input to the server.

[0534] Step 3:

[0535] The server stores the received data in a database and begins analysis. It analyzes the operation history data and sentiment data based on an AI model and calculates data correlations. Specifically, it uses the Python pandas library to analyze the emotional state of the user when performing a particular operation. The analysis results become input for the next processing step.

[0536] Step 4:

[0537] The server generates textual instructions and visual guides based on the analysis results. Here, a generative AI model is utilized, and natural language processing techniques are used to create manuals that are tailored to the user's emotions. The resulting manual is the output of this step.

[0538] Step 5:

[0539] The server provides the generated optimized manual to the user via the terminal. By referring to the updated manual, the user can operate the system more smoothly. At this point, the system provides support to further improve the user's experience.

[0540] (Application Example 2)

[0541] 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."

[0542] In robots used in living spaces, such as home assistive devices, there is a challenge in improving the user experience by providing improved instructional materials for operations that are difficult or stressful for the user. In this situation, conventional methods have the problem of not being able to generate adaptive instructional materials that take the user's emotions into consideration.

[0543] 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.

[0544] In this invention, the server includes means for analyzing operation history data and emotion data and converting operation procedures corresponding to the user's emotions into text; means for collecting screen display change information and creating a visual guide adapted to the user's emotions; means for analyzing user feedback and emotion data and updating and optimizing the document; and means for acquiring emotion data in real time. This makes it possible to provide dynamic and easy-to-understand instructional materials adapted to the user's emotional state.

[0545] "Operation history data" refers to information about the procedures and actions recorded when a user operates the system.

[0546] "Emotional data" refers to information that quantitatively or qualitatively represents a user's psychological state or emotions.

[0547] "A method for transcribing user operation procedures into text that respond to user emotions" refers to a technology that analyzes user operation history and emotion data to express operation procedures in a document that takes emotions into consideration.

[0548] "Screen display change information" refers to information about changes in elements or design on the screen.

[0549] "Means of creating visual guides" refers to techniques for generating designs and elements to visually provide instructions and explanations in a user interface.

[0550] "Feedback analysis" is the process of analyzing opinions and comments provided by users to extract areas for improvement and evaluations.

[0551] "Methods for updating and optimizing documents" refer to technologies for modifying existing documents based on new information and analysis results, making them more effective.

[0552] "Methods for acquiring emotional data in real time" refer to technologies that instantly sense a user's emotional state and collect it as data.

[0553] To implement this invention, three main elements—a server, a terminal, and a user—must function in an integrated manner. The server acts as the central hub for basic data processing, performing analysis using operation history data and sentiment data. Python is used for data analysis and optimization of operation procedures, while OpenCV is utilized for sentiment recognition. Furthermore, cloud services such as AWS Lambda are used to perform data processing on the server.

[0554] The device is responsible for acquiring emotional data in real time during user interaction and sending it to the server. To achieve this, an emotion engine operates via the device's built-in camera and microphone, analyzing the user's facial expressions and voice tone. An NVIDIA Jetson Nano is used for acquiring emotional data, while a Raspberry Pi is utilized for local data processing and communication.

[0555] When users receive assistance with daily life through consumer robots, they can enjoy an intuitive and comfortable operating experience thanks to optimized operation guides. For example, if a user expresses frustration with the operation of a household assistance robot's cleaning function, the server will suggest more easily understandable operating procedures based on that emotional data.

[0556] As an example of how this system can be applied, if a user expresses a desire to "efficiently complete cleaning for their pet," the system will use emotional data to generate improved guidance. An example of a prompt message might be, "The user expressed frustration with the operation. Please suggest improvements to this operation."

[0557] This technology is a measure to improve usability and enhance the effectiveness of consumer robots by dynamically providing guidance while taking user emotions into consideration.

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] When the device begins user interaction, it uses the camera and microphone in real time to sense the user's facial expressions and voice. The collected data is analyzed by an emotion engine and generated as emotion data indicating the user's emotional state. This emotion data is sent to the server along with the operation log. The input is the user's video and audio feeds, and the output is emotion data.

[0561] Step 2:

[0562] The server analyzes operation history data and sentiment data received from the terminal. Using Python, it analyzes this data and associates the user's emotions during operation with the operation steps. In this process, a generative AI model is used to generate prompts and create detailed text-based operation instructions. The input is operation history data and sentiment data, and the output is optimized text-based operation instructions.

[0563] Step 3:

[0564] The server creates a visual guide based on the collected screen display change information. Using OpenCV, it captures screenshots of the screen and associates them with operation steps and emotions, preparing an intuitive and easy-to-understand guide for the user. The input is the collected screen display change information, and the output is the visual guide.

[0565] Step 4:

[0566] The document is updated and further optimized by re-analyzing user feedback and continuously transmitted sentiment data. This provides users with more adaptive instructional materials for subsequent use. The input is user feedback and new sentiment data, and the output is the updated and optimized instructional material.

[0567] Step 5:

[0568] By allowing users to operate consumer robots based on optimized operating procedures and visual guides, an efficient experience becomes possible. A concrete example of user action would be a scenario where the robot cleans according to instructions while simultaneously addressing the user's needs. The output is an improved user experience and appropriate robot operation.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This invention describes a system for automating the generation and updating of manuals based on operations performed by users of a business system. This system mainly consists of three components: a server, a terminal, and a user.

[0587] The server functions as the core of data processing within the system, responsible for collecting, storing, and analyzing user operation history data. Based on this data, the server converts operation procedures into text, analyzes screen display changes, and creates visual guides tailored to various situations. It also utilizes natural language processing and machine learning technologies to update manuals by analyzing user feedback.

[0588] The terminal functions as the user interface, sending changes to the user interface to the server in real time. It is also used as a device to record user actions, for example, by automatically taking screenshots of the screen and sending them to the server. This makes it easy to collect material for providing visual support.

[0589] Users utilize the manuals generated by this system to assist in their daily work. They can proceed with operations based on the information provided by the system, performing tasks while referring to the manuals. Furthermore, users provide feedback as needed, contributing to improving the quality of the manuals.

[0590] As a concrete example, consider the case where a user needs to generate a procedure manual for managing product inventory when a new sales system is introduced. The server collects a history of inventory searches and changes performed by the user and uses that data to create the inventory management manual. The terminal records the screen state as the user interacts with it and sends image data showing the specific button locations and operation flow to the server.

[0591] In this way, the proposed system can improve operational efficiency by efficiently supporting a series of operations within the business system and automatically and continuously providing the latest manuals.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The user initiates an operation on the business system and uses a specific function (e.g., product search). The operation history is recorded on the terminal in real time.

[0595] Step 2:

[0596] The terminal sends recorded operation data to the server. This includes clicks, entered data, time information, and more.

[0597] Step 3:

[0598] The server analyzes the received operation history data and converts the operation procedures into text through AI processing. The generated text provides a detailed explanation of those procedures.

[0599] Step 4:

[0600] The device detects changes to the user interface (e.g., the addition of a new button) and sends this information to the server. This information includes the location and characteristics of the changed UI components.

[0601] Step 5:

[0602] Based on UI change information, the server generates new screenshots and videos as needed to create visual guides. These are then integrated into the operation manual.

[0603] Step 6:

[0604] Users view the generated manual and proceed with their tasks. They provide feedback on any difficulties in operation or points they do not understand.

[0605] Step 7:

[0606] The server collects user feedback and analyzes it using natural language processing. Based on the results, it improves the manual, adding details or making corrections where necessary.

[0607] Step 8:

[0608] The server distributes updated manuals throughout the entire system, ensuring that users always have access to the latest information.

[0609] (Example 1)

[0610] 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".

[0611] Traditional methods of creating operational manuals require manual recording and updating of procedures, which is time-consuming and labor-intensive. Furthermore, it is difficult to keep operating procedures and related visual materials up-to-date, resulting in insufficient support for users to perform their tasks accurately and efficiently.

[0612] 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.

[0613] In this invention, the server includes means for analyzing operation history information and converting work procedures into text, means for collecting screen display change information and creating visual guidance, and means for analyzing user feedback and updating documents. This enables automatic and immediate updating of work procedures and the provision of visually integrated support.

[0614] "Operation history information" refers to a record of a series of operations performed by a user on the system.

[0615] A "business procedure" is a description of the steps and sequence of operations necessary to perform a specific task.

[0616] "Text conversion" refers to the process of converting non-text data into textual information and recording it as a written document.

[0617] "Screen display change information" refers to data related to changes and updates that occur on the user interface.

[0618] "Visual guidance" refers to images and illustrated guides used to make operating procedures easier to understand.

[0619] "User feedback" refers to feedback and comments received from people who use the system.

[0620] "Document updating" refers to revising existing documents and manuals based on new information to keep them up-to-date.

[0621] A "terminal" refers to a device used by a user to access and operate a system.

[0622] A "server" refers to a core computing system that processes and manages data.

[0623] "Machine learning technology" is a branch of computer science that automates specific tasks by analyzing large amounts of data and identifying patterns.

[0624] A "generative AI model" refers to a model that uses artificial intelligence to generate new data and information based on a set task.

[0625] "Natural language processing technology" is a technology that uses computers to understand, analyze, and generate human language.

[0626] "Image data" refers to visual information such as screenshots and photographs, which are represented in digital format.

[0627] In an embodiment of this invention, the system consists of three main elements: a server, a terminal, and a user.

[0628] The server forms the core of this system. The server possesses powerful computing capabilities and aggregates and processes operation history information and screen display change information. The server uses natural language processing technology to convert operation history information into text and leverages machine learning technology to identify common operation patterns. Furthermore, the server uses a generative AI model to create visual guidance based on the generated information. The resulting business procedures are then compiled into documents provided to the user. In addition, the server analyzes user feedback and plays a role in continuously updating these documents.

[0629] To give a concrete example, when new software is introduced, the server records the operations performed by the user and analyzes that data to automatically generate an efficient operation manual. For instance, a prompt such as "Please create a manual for registering products using the new inventory management system" could be input into an AI model for generation.

[0630] The terminal is a device that records user actions and transmits that data to the server in real time. The terminal meticulously records user clicks and input actions and takes screenshots of the screen display. This provides the server with image data associated with specific operation steps, improving the quality of visual guidance.

[0631] If user actions are recorded by the terminal, for example, when a user registers new inventory in the system, the procedure is instantly sent to the server. As a result, the user can immediately refer to the new operation manual.

[0632] Users apply the visual guidance and procedural manuals provided by this system to their daily work and provide feedback to the server. When users utilize the new system's functions, they can improve operational efficiency by following the prompts provided by the generated AI model. Furthermore, users can contribute to the overall improvement of the system by providing feedback.

[0633] The implementation of this system will enable rapid information updates and the provision of accurate work procedures, contributing to improved work efficiency for users.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The terminal continuously records the operations performed by the user on the business system. Specifically, it collects logs of buttons clicked and data entered by the user, and automatically takes screenshots of the screen. This information is sent to the server as operation history information and image data.

[0637] Step 2:

[0638] The server receives operation history information sent from the terminal and stores it in a database. Using the input operation history information, the server utilizes natural language processing technology to convert the business procedures into text. This process outputs the specific operations performed by the user in an easy-to-understand format.

[0639] Step 3:

[0640] The server analyzes screen display change information and integrates screenshots obtained using image processing technology as visual guidance. It associates the input image data with operating procedures, enabling the user to visually understand the relevant steps. As a result, the necessary visual information for business procedures is output.

[0641] Step 4:

[0642] The server collects user feedback and analyzes that feedback. Based on the input feedback, it updates existing business procedure documents and incorporates new information and corrections using a generation AI model. This process outputs up-to-date business procedure documents that take into account feedback data from the entire system.

[0643] Step 5:

[0644] Users perform their daily tasks while viewing business procedure documents generated by the server on their terminals. Using prompts provided based on the generated AI model, users can quickly learn and execute new operations. As a result, work efficiency is improved.

[0645] (Application Example 1)

[0646] 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".

[0647] In factory machinery operation, manual manual updates and instruction checks reduce operational efficiency. Furthermore, providing real-time visual guidance is difficult in situations requiring rapid operation, resulting in frequent delays and errors. To overcome these problems and improve operational efficiency, a system is needed that enables automatic generation and updating of operating procedures and real-time instruction delivery.

[0648] 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.

[0649] In this invention, the server includes means for analyzing operation history data and converting operation procedures into text; means for collecting screen display change information and creating visual guides; means for analyzing user feedback and updating documents; and means for delivering real-time guides to operation devices to help users perform operations quickly. This enables improved efficiency and reduced errors in operation tasks.

[0650] "Operation history data" refers to information that records a series of operations performed by a user.

[0651] "Operating instructions" are a set of instructions that show the steps necessary to perform a specific task.

[0652] "Screen display change information" refers to information that indicates how the display state on the user interface has changed.

[0653] A "visual guide" is visual aid information provided to help users understand something.

[0654] "Feedback" refers to opinions and reactions provided by users, which are used to improve the system.

[0655] "Real-time guidance" refers to operational instructions provided to the user at the moment they need them.

[0656] An "operation device" is a device used by a user to receive and execute operational instructions.

[0657] The system for carrying out this invention consists of a server, a terminal, and a user.

[0658] The server, as the central function of the system, collects and analyzes operation history data and transcribes operation procedures into text. This process utilizes natural language processing techniques and machine learning algorithms. Specifically, data analysis and model execution are performed using the Python programming language.

[0659] The server also collects information on screen display changes and generates visual guides based on that information. Based on this information, it also provides real-time guidance to help users complete tasks quickly. User feedback is a critical source of information for improving the system's accuracy.

[0660] On the terminal, user actions are recorded through the interface, and screenshots of the screen are taken and sent to the server. Because this process involves image processing technology, libraries such as OpenCV may be used.

[0661] Users can perform tasks efficiently using the real-time guidance provided through this system. The system offers interactive operational assistance to users via devices such as smart glasses or tablets.

[0662] As a concrete example, when a user replaces a machine part during work, the server generates the latest operating procedure and displays it as a visual guide on smart glasses. At this time, a prompt such as "Generate a detailed visual guide for operators when new machinery is introduced in the factory" is input into the generating AI model. In this way, a system is realized that improves the efficiency of operational tasks and reduces errors.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The device detects user actions and records them in real time. Input includes user actions and clicks on the interface. Output is generated operation history data, which is sent to the server. Specifically, the device captures data with adjusted sensitivity and accumulates data when necessary events occur.

[0666] Step 2:

[0667] The server receives and analyzes the operation history from the terminal. The input is operation history data. Based on this data, the server uses natural language processing technology to generate text-based operation procedures. The output is the text-based operation procedures. Specifically, a generation AI model performs annotation and converts the data into text.

[0668] Step 3:

[0669] The server retrieves screen display change information from the terminal and creates a visual guide. The input is a screenshot of the changed screen. The output is image data for the visual guide. The server uses a specific algorithm to visualize the user's actions and other information.

[0670] Step 4:

[0671] Users provide feedback. Users input any feedback into the system via a terminal. The input is feedback data, and the output is recorded by the server to help improve the system. Specifically, the feedback is stored as text data.

[0672] Step 5:

[0673] The server delivers real-time guidance. Inputs are generated operating procedures and visual guides. Outputs are the provision of guidance to the operating device. The server processes the information in real time, generates dedicated prompts, and passes them to the operator.

[0674] This series of processes allows users to easily understand the operating procedures and perform tasks quickly.

[0675] 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.

[0676] This invention combines emotion recognition technology with a system that analyzes user operation history and feedback to provide up-to-date and user-optimized manuals. The system consists of a server, terminals, users, and an emotion engine.

[0677] The server, acting as the central hub for overall data processing, is responsible for collecting and analyzing operation history data and generating manuals. The server analyzes both operation logs and sentiment data received from terminals, correlating user emotions and the actions they performed. This allows for the creation of high-quality manuals that detail operation steps in text and include screen screenshots as visual guides.

[0678] The terminal uses an emotion engine to recognize the user's emotions in real time as they interact with the target system, and sends that data to the server. The terminal also records changes to the user interface and user actions, and transfers that data to the server.

[0679] Users can perform their tasks efficiently through the manuals provided using this system. If a user encounters difficulties during operation or shows an emotional reaction to a particular function, these details are captured by the emotion engine. This allows user experience-based feedback to be fed into the system and used to improve the manuals.

[0680] As a concrete example, consider the introduction of a new project management tool. If a user experiences emotional stress while scheduling tasks, this emotional data is recognized by an emotion engine via the terminal. The server identifies specific steps that caused high stress and improves the manual by providing more detailed explanations of those steps or adding visual guides to make them easier to understand. In this way, an emotion-conscious approach to manuals can increase user satisfaction.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] When a user begins using a specific function of a business system, the terminal monitors the user's actions in real time. Along with the actions, emotion data is acquired by an emotion engine using cameras, microphones, etc.

[0684] Step 2:

[0685] The device sends the acquired operation logs and emotional data to the server. The emotional data includes emotional states (e.g., stress, satisfaction) derived from the analysis of the user's facial expressions and voice.

[0686] Step 3:

[0687] The server analyzes the received data and identifies the operation steps where emotions have changed most significantly. At this stage, natural language processing techniques are used to generate the operation procedure, and explanations that take the emotional data into account are added.

[0688] Step 4:

[0689] The server uses UI information sent from the terminal to create a visual guide, associating screenshots of the screen display with the operation steps. The screenshots are marked to indicate the operation stage where emotional changes occurred.

[0690] Step 5:

[0691] Users utilize the generated manual to continue their work. The manual includes details of areas for improvement and steps that users deem emotionally important.

[0692] Step 6:

[0693] When users perform their tasks based on the generated manuals and provide feedback on parts of the manuals that are difficult to understand or on emotional burdens, that information is also collected through the terminal.

[0694] Step 7:

[0695] The server regularly updates the manual based on sentiment data, including user feedback, and operating procedures, improving it to be more user-friendly before redistributing it.

[0696] (Example 2)

[0697] 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".

[0698] In modern information systems, improving the user experience requires appropriate support and guidance based on user actions. However, traditional manuals often fail to adequately consider user emotions and operational situations, resulting in limited usability. Furthermore, updating documents to quickly incorporate feedback is not easy. Therefore, there is a need to develop flexible, real-time support methods that respond to user actions.

[0699] 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.

[0700] In this invention, the server includes means for collecting operation history data and emotion data and analyzing the user's emotional state, means for transcribing operation procedures into text and creating visual guides, and means for analyzing user feedback and updating the manual. This makes it possible to provide a manual optimized based on the user's individual emotional state in real time.

[0701] "Operation history data" refers to digital information that records a series of actions and procedures related to a user's system operations.

[0702] "Emotional data" refers to digitized information that indicates a user's emotional state, and is obtained based on the analysis of camera and voice data.

[0703] "Means of analysis" refer to the functions and methods used to process collected data, extract meaning from it, and make judgments and decisions.

[0704] "Text conversion" is the process of representing data and operating procedures as textual information.

[0705] "Visual guides" are visual elements such as images, diagrams, and screenshots that present information and operating procedures to users in an easy-to-understand manner.

[0706] "Feedback" refers to information provided by users, such as opinions, reactions, evaluations, and requests regarding operation.

[0707] "Means of updating the manual" refers to methods or functions for modifying or improving the content of the manual based on user feedback and new information.

[0708] "Means of delivery" refers to the functions and methods for presenting the final created manual or guide in a way that is accessible to users.

[0709] The system related to this invention is mainly composed of a server, a terminal, a user, and an emotion engine. The specific operation of each component will be described below.

[0710] The terminal functions as the interface for users to operate the system. It is equipped with a camera and microphone to record operations in real time and recognize user emotions using an emotion engine. Emotion recognition software generally uses algorithms that analyze both audio and video. For example, natural language processing is applied to speech recognition technology, and engines from Microsoft or APIs from Google are used.

[0711] The server centrally manages the received data and performs detailed analysis based on it. The server integrates operation history data and sentiment data and performs analysis using an AI model. This AI model uses natural language processing technology to generate text and visual guides that correspond to the user's emotional state. The data analysis uses Python's pandas library and a database management system (DBMS) such as MySQL.

[0712] This will give users access to always-updated manuals and guidelines. If a user experiences confusion or stress with a particular operation, that information will be fed back into the system in real time, allowing for more specific and personalized support. For example, if a user finds the instructions for using a new digital tool difficult to understand, that operation will be stored as sentiment data, and the next time they use it, the manual will be provided with more specific explanations.

[0713] A suitable example of a prompt would be, "Analyze which operations users found stressful while using the new tool, and generate suggestions for improving the manual for those operations." In this way, the overall system aims to continuously optimize the user experience in a way that takes emotions into account.

[0714] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0715] Step 1:

[0716] The device collects user actions and corresponding emotional data. When a user clicks the mouse or types on the keyboard, the action history is recorded as a log on the device. Simultaneously, the camera and microphone are used to collect the user's facial expressions and voice tone as emotional data. This information serves as initial input data.

[0717] Step 2:

[0718] The device sends the collected operation history data and sentiment data to the server. During transmission, the data is encrypted using the SSL / TLS protocol to ensure security. At this stage, the transmitted data becomes input to the server.

[0719] Step 3:

[0720] The server stores the received data in a database and begins analysis. It analyzes the operation history data and sentiment data based on an AI model and calculates data correlations. Specifically, it uses the Python pandas library to analyze the emotional state of the user when performing a particular operation. The analysis results become input for the next processing step.

[0721] Step 4:

[0722] The server generates textual instructions and visual guides based on the analysis results. Here, a generative AI model is utilized, and natural language processing techniques are used to create manuals that are tailored to the user's emotions. The resulting manual is the output of this step.

[0723] Step 5:

[0724] The server provides the generated optimized manual to the user via the terminal. By referring to the updated manual, the user can operate the system more smoothly. At this point, the system provides support to further improve the user's experience.

[0725] (Application Example 2)

[0726] 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".

[0727] In robots used in living spaces, such as home assistive devices, there is a challenge in improving the user experience by providing improved instructional materials for operations that are difficult or stressful for the user. In this situation, conventional methods have the problem of not being able to generate adaptive instructional materials that take the user's emotions into consideration.

[0728] 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.

[0729] In this invention, the server includes means for analyzing operation history data and emotion data and converting operation procedures corresponding to the user's emotions into text; means for collecting screen display change information and creating a visual guide adapted to the user's emotions; means for analyzing user feedback and emotion data and updating and optimizing the document; and means for acquiring emotion data in real time. This makes it possible to provide dynamic and easy-to-understand instructional materials adapted to the user's emotional state.

[0730] "Operation history data" refers to information about the procedures and actions recorded when a user operates the system.

[0731] "Emotional data" refers to information that quantitatively or qualitatively represents a user's psychological state or emotions.

[0732] "A method for transcribing user operation procedures into text that respond to user emotions" refers to a technology that analyzes user operation history and emotion data to express operation procedures in a document that takes emotions into consideration.

[0733] "Screen display change information" refers to information about changes in elements or design on the screen.

[0734] "Means of creating visual guides" refers to techniques for generating designs and elements to visually provide instructions and explanations in a user interface.

[0735] "Feedback analysis" is the process of analyzing opinions and comments provided by users to extract areas for improvement and evaluations.

[0736] "Methods for updating and optimizing documents" refer to technologies for modifying existing documents based on new information and analysis results, making them more effective.

[0737] "Methods for acquiring emotional data in real time" refer to technologies that instantly sense a user's emotional state and collect it as data.

[0738] To implement this invention, three main elements—a server, a terminal, and a user—must function in an integrated manner. The server acts as the central hub for basic data processing, performing analysis using operation history data and sentiment data. Python is used for data analysis and optimization of operation procedures, while OpenCV is utilized for sentiment recognition. Furthermore, cloud services such as AWS Lambda are used to perform data processing on the server.

[0739] The device is responsible for acquiring emotional data in real time during user interaction and sending it to the server. To achieve this, an emotion engine operates via the device's built-in camera and microphone, analyzing the user's facial expressions and voice tone. An NVIDIA Jetson Nano is used for acquiring emotional data, while a Raspberry Pi is utilized for local data processing and communication.

[0740] When users receive assistance with daily life through consumer robots, they can enjoy an intuitive and comfortable operating experience thanks to optimized operation guides. For example, if a user expresses frustration with the operation of a household assistance robot's cleaning function, the server will suggest more easily understandable operating procedures based on that emotional data.

[0741] As an example of how this system can be applied, if a user expresses a desire to "efficiently complete cleaning for their pet," the system will use emotional data to generate improved guidance. An example of a prompt message might be, "The user expressed frustration with the operation. Please suggest improvements to this operation."

[0742] This technology is a measure to improve usability and enhance the effectiveness of consumer robots by dynamically providing guidance while taking user emotions into consideration.

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] When the device begins user interaction, it uses the camera and microphone in real time to sense the user's facial expressions and voice. The collected data is analyzed by an emotion engine and generated as emotion data indicating the user's emotional state. This emotion data is sent to the server along with the operation log. The input is the user's video and audio feeds, and the output is emotion data.

[0746] Step 2:

[0747] The server analyzes operation history data and sentiment data received from the terminal. Using Python, it analyzes this data and associates the user's emotions during operation with the operation steps. In this process, a generative AI model is used to generate prompts and create detailed text-based operation instructions. The input is operation history data and sentiment data, and the output is optimized text-based operation instructions.

[0748] Step 3:

[0749] The server creates a visual guide based on the collected screen display change information. Using OpenCV, it captures screenshots of the screen and associates them with operation steps and emotions, preparing an intuitive and easy-to-understand guide for the user. The input is the collected screen display change information, and the output is the visual guide.

[0750] Step 4:

[0751] The document is updated and further optimized by re-analyzing user feedback and continuously transmitted sentiment data. This provides users with more adaptive instructional materials for subsequent use. The input is user feedback and new sentiment data, and the output is the updated and optimized instructional material.

[0752] Step 5:

[0753] By allowing users to operate consumer robots based on optimized operating procedures and visual guides, an efficient experience becomes possible. A concrete example of user action would be a scenario where the robot cleans according to instructions while simultaneously addressing the user's needs. The output is an improved user experience and appropriate robot operation.

[0754] 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.

[0755] 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.

[0756] In the above embodiment, an example was given in which the 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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 as being incorporated by reference.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A means of analyzing operation history data and converting operation procedures into text,

[0778] A means of collecting screen display change information and creating a visual guide,

[0779] A means of analyzing user feedback and updating documents,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, which generates a description of user operation procedures using natural language processing technology.

[0783] (Claim 3)

[0784] The system according to claim 1, which uses image processing technology to obtain screenshots of the screen display and manages them in association with the operation procedure.

[0785] "Example 1"

[0786] (Claim 1)

[0787] A means of analyzing operation history information and converting business procedures into text,

[0788] A means of collecting screen display change information and creating visual guidance,

[0789] A means of analyzing user feedback and updating documents,

[0790] A means of sending data from a terminal to a server and recording operations in real time,

[0791] A means for identifying operation patterns using machine learning techniques,

[0792] A method for generating natural and easy-to-understand text using a generative AI model,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, which uses natural language processing technology to generate a description of a business procedure by a user.

[0796] (Claim 3)

[0797] The system according to claim 1, which uses image processing technology to acquire image data of a screen display and manages it in association with business procedures.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] A means of analyzing operation history data and converting operation procedures into text,

[0801] A means of collecting screen display change information and creating a visual guide,

[0802] A means of analyzing user feedback and updating documents,

[0803] A means of delivering real-time guidance to operating devices to help users perform operations quickly,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which generates a description of user operation procedures using natural language processing technology.

[0807] (Claim 3)

[0808] The system according to claim 1, which uses image processing technology to acquire screenshots of the screen display, manages them in association with operation procedures, and presents them to an operation device.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A means of collecting operation history data and emotional data to analyze the user's emotional state,

[0812] A means of transcribing operating procedures into text and creating visual guides,

[0813] A means of analyzing user feedback and updating the manual,

[0814] A means of providing the generated manual to the user,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, which uses natural language processing technology to generate descriptions of operating procedures that match the user's emotional state.

[0818] (Claim 3)

[0819] The system according to claim 1, which uses image processing technology to acquire information displayed on the screen and manages it in association with an operation procedure.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] A means of analyzing operation history data and emotion data to convert operation procedures into text that correspond to the user's emotions,

[0823] A means of collecting screen display change information and creating visual guides that adapt to the user's emotions,

[0824] A means of analyzing user feedback and sentiment data to update and optimize documents,

[0825] A means of acquiring emotional data in real time,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which uses natural language processing technology to generate a description of user operation procedures and adaptively changes the content based on sentiment data.

[0829] (Claim 3)

[0830] The system according to claim 1, which uses image processing technology to obtain screenshots of the screen display and manages them in relation to the user's emotional state. [Explanation of Symbols]

[0831] 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. A means of analyzing operation history data and converting operation procedures into text, A means of collecting screen display change information and creating a visual guide, A means of analyzing user feedback and updating documents, A means of delivering real-time guidance to operating devices to help users perform operations quickly, A system that includes this.

2. The system according to claim 1, which generates a description of user operation procedures using natural language processing technology.

3. The system according to claim 1, which uses image processing technology to acquire screenshots of the screen display, manages them in association with operation procedures, and presents them to an operation device.

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