system

Abstract

We provide the system. [Solution] Means of acquiring business data, A means of automatically generating business procedures by analyzing acquired business data, A means of updating the generated procedure manual based on user feedback, A means of saving and sharing updated procedure manuals in a knowledge base, A means of learning and improving the analysis model using user feedback, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to standardize and improve the efficiency of business processes, it is necessary to update the procedure manuals in response to frequent business changes. However, the conventional methods have problems such as requiring a great deal of labor for creating and updating the procedure manuals and reducing the business efficiency. Also, in the education of new employees and technology inheritance, it is difficult to share knowledge systematically, so there is also a problem that human errors are likely to increase. By solving these problems, it is desired to improve business efficiency and strengthen knowledge management within the organization.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates business procedures by acquiring business data and analyzing that data using an advanced analytical model. This system includes a function to continuously update the generated procedures based on user feedback. Furthermore, the updated procedures can be stored in a central knowledge base and shared across departments as needed. In addition, user feedback is used as training data for the analytical model, improving the model's accuracy and enabling rapid response to changes in business operations. This improves overall organizational efficiency and promotes knowledge management and sharing.

[0006] "Business data" refers to various documents and data containing information related to business processes and procedures within a company or organization.

[0007] "Analysis" is the process of analyzing acquired business data, understanding its content, and extracting effective business procedures.

[0008] "Automatic generation" refers to the process of automatically creating procedure manuals using a system based on analyzed data.

[0009] A "procedure manual" is a document that explicitly outlines work procedures and methods, and its purpose is to standardize and streamline operations.

[0010] "Feedback" refers to the opinions and evaluations that users provide regarding the generated procedure manuals, and it serves as a source of information for the system to improve the manuals based on this feedback.

[0011] A "knowledge base" is a collection of information shared across an entire organization, a database where updated procedures and related information are stored.

[0012] "Learning" is the process of improving the performance of an analysis model based on user feedback. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more nonvolatile storage devices that store various programs and various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that effectively manages various business-related data and automatically generates and updates business procedures. This system primarily consists of three elements: a server, users, and terminals. Specific embodiments are described below.

[0035] System Configuration

[0036] Users input work-related documents using a terminal and send them to the server. The server analyzes the acquired work data and automatically generates work procedures using an AI model. In this process, the server processes the data using natural language and generates procedure manuals based on the analyzed information. The generated procedure manuals are then continuously improved through user feedback.

[0037] Update of the procedure manual

[0038] When a user submits feedback from their device, the server receives the information and incorporates it into the procedure manual. The server updates the procedure manual in real time and incorporates new business data as needed.

[0039] Knowledge-based management

[0040] Updated procedures are stored in a central knowledge base. This allows the server to share information across departments. Furthermore, the stored information is arranged to be easily accessible to other departments and relevant teams.

[0041] Learning and Improvement

[0042] User feedback is used as training data for the analysis model. The server updates the model based on this data, improving the accuracy of generating business procedures. This leads to improved operational efficiency across the organization and enhanced knowledge sharing.

[0043] Specific example

[0044] Consider a manufacturing company where production procedures are required when launching a new product line. The user inputs product specifications and protocols into a terminal and sends them to a server. The server analyzes this data and automatically generates step-by-step manufacturing procedures. The user reviews these procedures and provides feedback from the factory floor. The server incorporates the feedback, updates the procedures, and stores them in a knowledge base. This allows other project teams within the company to access the information, leading to standardization and increased efficiency in operations.

[0045] In this way, by systematically integrating everything from the analysis of business data to the automation of procedures and the update process, a system is built that promotes organizational knowledge management and improves operational efficiency.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users prepare work-related documents and data files on their terminals and upload them to the system. This data may include requirements specifications, work procedures, and past work performance data.

[0049] Step 2:

[0050] The terminal transfers the uploaded data to the server. The server confirms that it has successfully received the data and then proceeds to the next analysis process.

[0051] Step 3:

[0052] The server analyzes the received business data using a generative AI model. Using natural language processing techniques, it extracts business procedures, related tasks, and points to note from each document. Based on this information, it constructs a preliminary model of the business procedures.

[0053] Step 4:

[0054] The server automatically generates procedure manuals in template format based on the extracted information. These manuals include key steps, detailed explanations, and, if necessary, multimedia content.

[0055] Step 5:

[0056] Users review the generated procedure manuals and provide feedback on areas that need improvement or additional information. In this process, users send comments and revision requests to the server using their terminals.

[0057] Step 6:

[0058] The server analyzes the feedback received from users and incorporates it into the procedure manual. If necessary, it adjusts the work procedures and content, updating the manual.

[0059] Step 7:

[0060] The server stores updated procedures in a knowledge base. This knowledge base is shared among departments and project teams and used as an information resource for the entire organization.

[0061] Step 8:

[0062] The server updates the learning algorithm of the generated AI model based on user feedback. Through this learning process, the model's accuracy is improved and reflected in subsequent business data analysis and procedure manual generation.

[0063] (Example 1)

[0064] 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."

[0065] In modern business operations, it is essential to efficiently manage relevant information and to quickly create and update operational guidelines. However, manually creating and updating operational guidelines is time-consuming and labor-intensive, and sharing information between different departments is difficult. Furthermore, there is a need for methods to effectively incorporate user feedback into operational guidelines and improve analytical models.

[0066] 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.

[0067] In this invention, the server includes means for analyzing business-related information using natural language processing technology, means for updating business guidelines based on user feedback, and means for storing and transmitting the updated business guidelines using a centralized management database. This enables efficient business management, rapid information sharing, and continuous improvement of the analysis model.

[0068] "Business-related information" refers to data, documents, specifications, protocols, etc., necessary for carrying out business operations within a corporation or organization.

[0069] "Natural language processing technology" refers to the technology that enables computers to understand and analyze the language that humans use on a daily basis.

[0070] "Business guidelines" refer to documents that describe the procedures and methods for performing a specific task.

[0071] "User feedback" refers to feedback information such as suggestions for improvement or requests for corrections regarding business guidelines and systems.

[0072] A "centralized management database" refers to an information system that centrally stores and manages data within a company or organization, and provides access to it as needed.

[0073] An "analytical model" refers to a mathematical or algorithmic structure used to analyze data and make predictions or decisions based on the results.

[0074] Modes for carrying out the invention

[0075] This invention is a system for efficiently managing various business-related information and for automatically generating and updating business guidelines. Specific embodiments focusing on the server, terminal, and user are described below.

[0076] Initial data entry and transmission

[0077] Users input work-related information through a terminal. This information includes specifications and business protocols. The system is designed to allow intuitive input using a GUI (Graphical User Interface). The terminal sends the entered data to the server. The data is transferred in JSON or XML format via a secure communication protocol.

[0078] Data analysis and generation of operational guidelines

[0079] The server analyzes the received business information using a natural language processing engine (e.g., Python's NLTK or spaCy). This analysis extracts important information necessary for business guidelines. The server then automatically generates business guidelines based on the extracted information using a generative AI model (e.g., GPT). The generated guidelines are output in PDF or text format and provided to the user.

[0080] Gathering feedback and updating guidelines

[0081] Users review the provided operational guidelines and provide feedback from their terminals to the server. This feedback includes specific additions, such as "additional explanations are needed for the assembly steps." The server analyzes the feedback and updates the operational guidelines regularly. The updated guidelines are stored in a centralized management database, enabling immediate information sharing within the organization.

[0082] Knowledge integration and sharing

[0083] The server centralizes updated operational guidelines in a centralized management database to ensure knowledge consolidation. The stored data is designed for easy access by other departments and relevant teams, facilitating efficient information sharing across departments.

[0084] Specific example

[0085] Consider a set of operational guidelines to be used when launching a new product line in a manufacturing company. The user inputs the specifications of the new product into a terminal and sends them to a server. The server uses natural language processing to analyze the input and generate operational guidelines that include specific manufacturing procedures. These guidelines include specific steps such as "Step 1: Material preparation" and "Step 2: Part assembly." If the user sends feedback such as "Additional safety precautions are needed for the part assembly step," the server updates the operational guidelines based on that feedback. By continuing this cycle, the efficiency and quality of operations will improve.

[0086] Example of a prompt

[0087] Please generate manufacturing guidelines for the new product XYZ. The specifications should include the following items: materials, dimensions, and assembly procedure.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The user inputs work-related information into the terminal. Specifically, they input specifications and protocols, and the terminal sends this to the server in JSON or XML format. The input data contains detailed information necessary for the work, and the output is the appropriate formatting and transfer of this data to the server.

[0091] Step 2:

[0092] The server analyzes data received from terminals using a natural language processing engine. Specifically, it uses Python's NLTK and spaCy to extract important keywords and phrases from the data. The input is formatted business information, and the output is analyzed structured data. Based on this data, the subsequent business guideline generation process begins.

[0093] Step 3:

[0094] The server inputs the extracted information as prompts into a generation AI model, which then automatically generates business guidelines. Specifically, it uses OpenAI's GPT and other technologies to output the generated business guidelines in text or PDF format. The input is structured data, and the output is a document containing business guidelines.

[0095] Step 4:

[0096] Users review the generated operational guidelines on their terminals. Specifically, they verify the content of the guidelines and input necessary corrections or improvement requests as feedback. The input is the operational guideline document, and the output is the feedback information.

[0097] Step 5:

[0098] The server receives user feedback and updates the operational guidelines. Machine learning algorithms are used to analyze the feedback and improve the guidelines. The input is feedback information, and the output is the updated operational guidelines. This update process refines the guidelines over time.

[0099] Step 6:

[0100] The server stores the updated business guidelines in a centralized management database. Specifically, this involves using SQL or NoSQL databases to establish a structure for storing, accessing, and sharing data. The input is the updated business guidelines, and the output is storage in the knowledge base and information sharing within the organization.

[0101] (Application Example 1)

[0102] 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."

[0103] In modern manufacturing, there is a demand for efficient management of operational information, optimization of work procedures, and improvement of product quality. In particular, developing automated systems capable of handling complex operational protocols is a challenge. However, conventional systems have limited efficient work operations and interdepartmental information sharing due to insufficient information acquisition, analysis, and feedback for improvement.

[0104] 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.

[0105] In this invention, the server includes means for acquiring business information, means for analyzing the acquired business information to automatically generate work procedures, means for updating the generated procedure manuals based on user feedback, means for storing and sharing the updated procedure manuals in a knowledge record, means for learning and improving the analysis model using user feedback, and means for the work machine to execute the work procedures based on product data and business protocols. This enables optimization of business processes and rapid information sharing.

[0106] "Business information" refers to all data related to the operation of a company or organization, including detailed information related to work procedures and product specifications.

[0107] A "work procedure" is a record that shows the specific steps and operations necessary to perform a particular task, and is compiled to improve work efficiency.

[0108] "Users" refer to individuals or teams who utilize this system for their work and contribute to improving the system's accuracy through feedback.

[0109] A "knowledge record" refers to a database or archive intended for sharing information within and outside an organization, preserving and making accessible accumulated knowledge.

[0110] "Feedback" refers to suggestions for improvement and opinions based on work performance provided by users, which are then used to improve the system's functionality.

[0111] "Working machinery" refers to hardware devices used to automate and perform specific manufacturing or processing tasks.

[0112] A "business protocol" refers to a set of instructions that define standardized procedures and rules necessary for carrying out business operations.

[0113] An "analysis model" refers to an algorithm or framework that processes business information using a computer program and creates products based on the analysis results.

[0114] The system for implementing this invention mainly consists of a server, a terminal, and a work machine. The server acquires business information and uses Python libraries such as NLTK and Spacy to analyze the data using natural language processing technology. The analyzed data is used to automatically generate work procedures using a generative AI model that utilizes TENSORFLOW® and PyTorch.

[0115] The terminal is a device that receives input and feedback from users and sends the feedback information to the server. The server updates the work procedures based on the feedback, saves the results in a knowledge record, and organizes it in a format that can be shared within the company. This facilitates information sharing within the organization and contributes to the optimization of subsequent business processes.

[0116] The machines operate based on automatically generated work procedures received from the server, and are responsible for performing physical manufacturing tasks. This leads to increased efficiency in business processes and improved product quality.

[0117] As a concrete example, consider a scenario where a new bread manufacturing process is introduced in a factory. Users input new product specifications and operational protocols via a terminal and send them to a server. The server analyzes the information and sends automatically generated work procedures to the machines. The machines then begin the manufacturing process according to these procedures, and the procedures are improved in real time based on user feedback.

[0118] An example of a prompt for a generative AI model is: "Analyze the user's input data and generate a new dough processing procedure for bread making. The feedback was to shorten the mixing time, but please adjust it to maintain quality."

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users input business information using a terminal and send it to the server. This input information includes product specifications and business protocols. This input data is preprocessed on the server. For example, it is converted into a data format that facilitates analysis through normalization and tokenization of text data.

[0122] Step 2:

[0123] The server analyzes pre-processed business information and extracts necessary information using natural language processing techniques. This process includes named entity recognition and dependency analysis using NLTK and Spacy. The extracted information is input into a generative AI model. The generative AI model automatically generates work procedures based on the input data and sends the results to the next process.

[0124] Step 3:

[0125] The server receives the generated work procedure and sends it to the terminal. The user reviews the work procedure on the terminal and provides feedback as needed. Examples of feedback include suggestions for improving work efficiency and points to adjust the procedure. Feedback from the user is then entered.

[0126] Step 4:

[0127] Upon receiving feedback, the server updates the work procedure using the generated AI model. The updated procedure is an improved version that reflects user feedback. This improvement process involves continuous learning, improving the model's accuracy. The updated procedure is also saved in the knowledge log.

[0128] Step 5:

[0129] The server transmits updated work instructions to the machine. The machine performs the physical tasks according to the instructions. The performed tasks are required to be efficient in the product manufacturing process. Optimized procedures are used in this implementation.

[0130] 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.

[0131] This invention aims to more effectively manage business processes and generate and improve business procedures that take into account the emotional state of users. Specific embodiments of this system are described below.

[0132] System Configuration

[0133] This system primarily consists of a server, users, terminals, and an emotion engine. Users send work-related documents to the server via their terminals. The server uses a generative AI model to analyze the work data and automatically generate work procedure manuals. The emotion engine also analyzes the user's emotions and utilizes this information in the generation process.

[0134] Analysis of emotional data

[0135] The terminal captures the user's facial expressions and tone of voice through emotion recognition technology when the user views the work procedure manual. The server analyzes this emotion data to evaluate how the user feels about the manual. The evaluation results are used to optimize the content and expression of the manual.

[0136] Generating and updating procedure manuals

[0137] Based on information from the emotion engine, the server fine-tunes the instructions to ensure users can easily understand them. When user feedback is received, the server updates the instructions, and the emotion engine re-evaluates the user's stress level and satisfaction.

[0138] Reflection in the knowledge base

[0139] The updated procedure manuals are stored in the knowledge base. This facilitates information sharing between departments and project teams. Furthermore, the knowledge base contributes to the standardization and efficiency of work procedures.

[0140] Specific example

[0141] For example, when a customer support team reviews their procedures, the emotion engine detects when a user feels frustrated or confused while reading the manual. The server then rephrases the manual's explanation in a more understandable way and adds relevant multimedia content based on the detected emotion. As a result, support staff can perform their duties with reduced stress, and the quality of support improves.

[0142] In this way, by integrating and managing business data and user sentiment data, and realizing a dynamic and user-centered business procedure management system, it is possible to aim for improved operational efficiency and user experience across the entire organization.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user uploads work-related documents to the terminal. The terminal then sends these documents to the server and prepares to begin data processing.

[0146] Step 2:

[0147] The server applies an AI model to analyze the received business data. This model uses natural language processing techniques to analyze documents and identify business flows and procedures. The analysis results are stored in a template format.

[0148] Step 3:

[0149] The terminal collects user emotional data through an emotion engine while the user is viewing work procedure manuals. This process infers stress levels and satisfaction levels from the user's facial expressions, tone of voice, and other factors.

[0150] Step 4:

[0151] The server analyzes data from the emotion engine and reviews the content of the procedure manual. Based on the user's emotions, it adjusts the wording and tone of the procedure and adds supplementary information as needed.

[0152] Step 5:

[0153] Users utilize the generated instructions and provide feedback via their devices as needed. This feedback includes specific comments and suggestions for improvements.

[0154] Step 6:

[0155] The server updates the procedure manual based on data from the feedback and sentiment engine. These updates may include optimizing business processes and refining specific wording.

[0156] Step 7:

[0157] The server stores updated procedures in a knowledge base. This knowledge base is used for information sharing within the company and is accessible to other employees and departments.

[0158] Step 8:

[0159] The server uses an emotion engine to improve its analysis model. It works with feedback data to train the model to improve the accuracy of generating and updating business procedures.

[0160] (Example 2)

[0161] 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".

[0162] While documenting and standardizing business procedures contributes to improved organizational productivity, traditional methods struggle to consider users' emotional states, resulting in procedural documents that are difficult for users to understand. Furthermore, efficient evaluation and updating methods are needed when utilizing user feedback to improve procedures. Additionally, a system is required to share updated information across the entire organization.

[0163] 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.

[0164] In this invention, the server includes means for acquiring business information, means for sensing the user's facial expressions and tone of voice to collect emotional states, and means for extracting key points of business procedures and proposing optimizations using a generative AI model. This enables the generation of business procedure manuals that take user emotions into consideration, as well as the sharing and updating of information across the entire organization.

[0165] "Business information" refers to all data related to business operations performed within an organization, including procedures, records, input data, and user feedback.

[0166] A "procedure document" is a document that describes the steps and procedures necessary to carry out a task, and its purpose is to improve work efficiency and standardize operations.

[0167] "User facial expressions and tone of voice" refer to the nonverbal expressions and vocal characteristics that users exhibit when using business documents, and are used to estimate the user's emotional state.

[0168] "Emotional information" refers to data about a user's emotional responses and psychological state, obtained through analysis of facial expressions and tone of voice.

[0169] A "generative AI model" is a machine learning model that uses advanced artificial intelligence technologies to analyze data and automatically generate and update procedural documents.

[0170] "To propose an optimization" means to find the most efficient or effective method or means under specific conditions and present it in a feasible form.

[0171] A "knowledge base" is a database system that centrally manages and stores business procedures and related information, enabling information sharing within an organization.

[0172] The specific configuration and operation of this system are shown to illustrate embodiments for carrying out the invention.

[0173] This system aims to generate and optimize business procedure manuals and consists mainly of a server, terminals, and users. The server is equipped with a high-performance processor and sufficient memory, and a generative AI model runs on it to analyze business data. The generative AI model receives business information provided by users and automatically generates procedure documents based on that data. Furthermore, the model optimizes the document content by incorporating emotional information into the model.

[0174] The device has software installed that detects the user's facial expressions and tone of voice, and is equipped with the ability to capture this data through the camera and microphone. The device sends this data to a server, which analyzes it and evaluates the user's emotional state. This makes it possible to generate procedural documents that take the user's emotions into consideration.

[0175] For example, if a user is using a new product's shipping procedure manual and encounters an unclear step, causing them to show confusion, the terminal will detect this expression. The server will then use this emotional information to create a prompt instructing the generative AI model to "update the steps to be concise and clear," thereby revising the procedure manual. In this way, user feedback is reflected in the procedure manual in real time.

[0176] As a concrete example, a prompt could be issued to the generating AI model with the instruction, "Update the product shipping procedure manual to be concise and clear, and make improvements based on sentiment data." Based on this instruction, the server analyzes business information and improves the procedure document.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] Users prepare work-related documents via their terminals and send them to the server. The input consists of work-related information, which may include existing procedures and feedback. Specifically, the user selects a document and presses the send button.

[0180] Step 2:

[0181] The server performs data analysis by inputting received business information into a generating AI model. The model analyzes the business information, extracts the key points of the procedure manual, and generates an initial procedure document. The output is the generated procedure document. Specifically, the model summarizes the text and organizes the procedures.

[0182] Step 3:

[0183] The device uses its camera and microphone to capture facial expressions and voice tone as the user views generated procedure documents. Input is non-verbal data from the user, and output is sent to the server as emotional information. The device captures information in real time.

[0184] Step 4:

[0185] The server analyzes emotional information transmitted from the terminal and evaluates the user's emotional state. The analysis reveals the user's psychological response to the procedural document. The input is emotional information, and the output is the user's emotional evaluation. Specifically, the server uses an emotional recognition algorithm.

[0186] Step 5:

[0187] The server sends prompts to the generative AI model based on sentiment evaluation, and adjusts the procedure document. For example, if the user shows a confused reaction, it sends a prompt to "simplify the steps." The input is the prompt, and the output is the updated procedure document. Specifically, the server highlights key points and adds explanations.

[0188] Step 6:

[0189] The updated procedure document is stored in the knowledge base and shared within the organization. The input is the updated procedure document, and the output is the addition of an entry to the knowledge base. The server sends the data to the knowledge management system, and the information is stored.

[0190] This series of steps ensures that operational procedures are always kept up-to-date and user-friendly, and enables efficient information sharing throughout the organization.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] Traditional business procedure systems have struggled to provide dynamic procedures that take into account user emotions and states, resulting in limited improvements to the user experience. This can lead to decreased operational efficiency and lower customer satisfaction, highlighting the need for adaptive and user-centered procedure management systems.

[0194] 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.

[0195] In this invention, the server includes means for acquiring business data, means for analyzing the acquired business data to automatically generate business procedures, means for updating the generated procedure manuals based on user feedback, means for analyzing an individual's emotional state using emotion recognition technology, and means for adaptively changing the business procedures based on the analyzed emotional state. This makes it possible to reflect the user's emotional state in real time and provide optimized procedure manuals.

[0196] "Business data" refers to data that includes information related to work and transactions, and serves as basic information for systems to process and analyze.

[0197] "Business procedures" are a set of steps and instructions that outline how to perform a specific task, serving as a guideline for efficient and effective work execution.

[0198] A "procedure manual" is a document that describes in detail the steps and methods for performing a task, serving as a guideline to help employees understand their work.

[0199] A "knowledge base" is a database that stores information and procedures related to business operations, and is used for sharing and reusing them throughout the organization.

[0200] "Emotion recognition technology" is a technology that analyzes an individual's facial expressions, voice, and behavior to determine their emotions, and is a means for computers to understand human emotions.

[0201] "Emotional state" refers to the state in which an individual exhibits psychological and physiological responses in a particular situation, and is a factor that significantly influences the user experience.

[0202] "Adaptive modification" refers to a method where the system automatically adjusts and updates itself in response to changes in circumstances and users, thereby achieving optimal results.

[0203] The system implementing this invention mainly consists of a server, an emotion recognition terminal, and a user. The server receives business data and automatically generates business procedures using a generative AI model. The emotion recognition terminal analyzes the user's facial expressions and tone of voice as they use the procedure manual and evaluates their emotions in real time. The server takes in the user's emotional state and feedback and dynamically adjusts the business procedures based on this information.

[0204] Specifically, the emotion recognition terminal uses smart glasses, allowing users to perform tasks while visually obtaining information. The software implements EmotionAPI and real-time voice tone analysis as emotion recognition technologies, thereby precisely determining the user's emotions. The generative AI model uses the latest natural language processing technology to provide work procedures that respond to user feedback and emotional states.

[0205] For example, when introducing a new product in a physical store, if emotion recognition technology detects that the user is confused, the server can use a generative AI model to generate a "prompt message to provide a clearer explanation" and display it through the emotion recognition terminal. This allows the sales staff to quickly provide appropriate explanations to capture the customer's attention, thereby improving the customer experience.

[0206] An example of a prompt for the generating AI model would be, "Provide a simplified explanation for a customer who seems confused about the new product features," which helps generate appropriate guidance based on the user's situation.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device captures the user's facial expressions and voice tone in real time. It takes raw data from the camera and microphone as input. This data is analyzed using emotion recognition technology to output the user's emotional state (e.g., satisfaction, confusion, anxiety). Specifically, it uses the EmotionAPI to identify emotions from facial muscle movements and voice intonation.

[0210] Step 2:

[0211] The server automatically generates appropriate work procedure manuals using a generative AI model based on acquired emotional state data. The input consists of the user's emotional state and related work data. The generative AI model analyzes this data to generate optimal explanations and customer service procedures. The output is a procedure manual that is easy for the user to understand. The system generates prompt statements based on the emotional data, and the content corresponding to these prompts is reflected in the procedure manual.

[0212] Step 3:

[0213] The terminal displays the generated procedure manual, allowing the user to visually confirm it. The input is the content of the procedure manual sent from the server. As output, the procedure manual is displayed on a screen such as smart glasses in a user-friendly format. Specifically, the procedure manual, including supplementary explanations tailored to the user's emotions, is presented on the screen.

[0214] Step 4:

[0215] Users perform tasks based on the displayed instructions and input feedback into the terminal as needed. This feedback is sent to the server and used to improve future instructions. Input methods include feedback forms and voice input, and output is points for improvement in the instructions. Operationally, user feedback is recorded in a digital form and stored on the server.

[0216] Step 5:

[0217] The server analyzes user feedback and compares it with emotional states to improve future procedures. Inputs are past emotional data and feedback data. Output is an updated analysis model. Specifically, a generative AI model learns from the feedback and works to generate more effective prompts.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system that effectively manages various business-related data and automatically generates and updates business procedures. This system primarily consists of three elements: a server, users, and terminals. Specific embodiments are described below.

[0235] System Configuration

[0236] Users input work-related documents using a terminal and send them to the server. The server analyzes the acquired work data and automatically generates work procedures using an AI model. In this process, the server processes the data using natural language and generates procedure manuals based on the analyzed information. The generated procedure manuals are then continuously improved through user feedback.

[0237] Update of the procedure manual

[0238] When a user submits feedback from their device, the server receives the information and incorporates it into the procedure manual. The server updates the procedure manual in real time and incorporates new business data as needed.

[0239] Knowledge-based management

[0240] Updated procedures are stored in a central knowledge base. This allows the server to share information across departments. Furthermore, the stored information is arranged to be easily accessible to other departments and relevant teams.

[0241] Learning and Improvement

[0242] User feedback is used as training data for the analysis model. The server updates the model based on this data, improving the accuracy of generating business procedures. This leads to improved operational efficiency across the organization and enhanced knowledge sharing.

[0243] Specific example

[0244] Consider a manufacturing company where production procedures are required when launching a new product line. The user inputs product specifications and protocols into a terminal and sends them to a server. The server analyzes this data and automatically generates step-by-step manufacturing procedures. The user reviews these procedures and provides feedback from the factory floor. The server incorporates the feedback, updates the procedures, and stores them in a knowledge base. This allows other project teams within the company to access the information, leading to standardization and increased efficiency in operations.

[0245] In this way, by systematically integrating everything from the analysis of business data to the automation of procedures and the update process, a system is built that promotes organizational knowledge management and improves operational efficiency.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] Users prepare work-related documents and data files on their terminals and upload them to the system. This data may include requirements specifications, work procedures, and past work performance data.

[0249] Step 2:

[0250] The terminal transfers the uploaded data to the server. The server confirms that it has successfully received the data and then proceeds to the next analysis process.

[0251] Step 3:

[0252] The server analyzes the received business data using a generative AI model. Using natural language processing techniques, it extracts business procedures, related tasks, and points to note from each document. Based on this information, it constructs a preliminary model of the business procedures.

[0253] Step 4:

[0254] The server automatically generates procedure manuals in template format based on the extracted information. These manuals include key steps, detailed explanations, and, if necessary, multimedia content.

[0255] Step 5:

[0256] Users review the generated procedure manuals and provide feedback on areas that need improvement or additional information. In this process, users send comments and revision requests to the server using their terminals.

[0257] Step 6:

[0258] The server analyzes the feedback received from users and incorporates it into the procedure manual. If necessary, it adjusts the work procedures and content, updating the manual.

[0259] Step 7:

[0260] The server stores updated procedures in a knowledge base. This knowledge base is shared among departments and project teams and used as an information resource for the entire organization.

[0261] Step 8:

[0262] The server updates the learning algorithm of the generated AI model based on user feedback. Through this learning process, the model's accuracy is improved and reflected in subsequent business data analysis and procedure manual generation.

[0263] (Example 1)

[0264] 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."

[0265] In modern business operations, it is essential to efficiently manage relevant information and to quickly create and update operational guidelines. However, manually creating and updating operational guidelines is time-consuming and labor-intensive, and sharing information between different departments is difficult. Furthermore, there is a need for methods to effectively incorporate user feedback into operational guidelines and improve analytical models.

[0266] 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.

[0267] In this invention, the server includes means for analyzing business-related information using natural language processing technology, means for updating business guidelines based on user feedback, and means for storing and transmitting the updated business guidelines using a centralized management database. This enables efficient business management, rapid information sharing, and continuous improvement of the analysis model.

[0268] "Business-related information" refers to data, documents, specifications, protocols, etc., necessary for carrying out business operations within a corporation or organization.

[0269] "Natural language processing technology" refers to the technology that enables computers to understand and analyze the language that humans use on a daily basis.

[0270] "Business guidelines" refer to documents that describe the procedures and methods for performing a specific task.

[0271] "User feedback" refers to feedback information such as suggestions for improvement or requests for corrections regarding business guidelines and systems.

[0272] A "centralized management database" refers to an information system that centrally stores and manages data within a company or organization, and provides access to it as needed.

[0273] An "analytical model" refers to a mathematical or algorithmic structure used to analyze data and make predictions or decisions based on the results.

[0274] Modes for carrying out the invention

[0275] This invention is a system for efficiently managing various business-related information and for automatically generating and updating business guidelines. Specific embodiments focusing on the server, terminal, and user are described below.

[0276] Initial data entry and transmission

[0277] The user inputs information related to the business through the terminal. This information includes specifications and business protocols. The information is designed to be intuitively input using a GUI (Graphical User Interface). The terminal sends the input data to the server. The data is transferred in JSON or XML format through a secure communication protocol.

[0278] Data Analysis and Generation of Business Guidelines

[0279] The server analyzes the received business information by leveraging natural language processing engines (e.g., NLTK or spaCy in Python). Through this analysis, important information necessary for business guidelines is extracted. The server automatically generates business guidelines based on the extracted information using a generative AI model (e.g., GPT). The generated guidelines are output in PDF or text format and provided to the user.

[0280] Collection of Feedback and Update of Guidelines

[0281] The user reviews the provided business guidelines and provides feedback from the terminal to the server. This feedback includes specific supplements such as "Additional explanations are needed for the assembly steps". The server analyzes the feedback and updates the business guidelines promptly. The updated guidelines are stored in a centralized management database, enabling immediate information sharing within the organization.

[0282] Integration and Sharing of Knowledge

[0283] The server stores the updated business guidelines in a centralized management database to perform knowledge integration. The stored data is designed to be easily accessible to other departments and related teams, thus promoting efficient information sharing among multiple departments.

[0284] Specific Example

[0285] Consider the business guidelines used when launching a new product line in a manufacturing company. The user inputs the product specifications into a terminal and sends them to the server. The server performs analysis using natural language processing and generates business guidelines that include specific manufacturing procedures. These guidelines describe specific procedures such as "Step 1: Prepare materials" and "Step 2: Assemble parts". When the user sends feedback stating that "additional safety precautions are required for the part assembly step", the server updates the business guidelines based on this feedback. By continuing such a cycle, the efficiency and quality of the business are improved.

[0286] Example of a prompt sentence

[0287] Please generate the manufacturing business guidelines for the new product XYZ. The specification includes the following items: materials, dimensions, and assembly procedures.

[0288] The flow of the specific process in Example 1 will be described using Figure 11.

[0289] Step 1:

[0290] The user inputs information related to the business into the terminal. As a specific operation, the user inputs the specification and protocol, and the terminal sends this to the server in JSON or XML format. The input data contains the detailed information required for the business, and the output is to appropriately format this and transfer it to the server.

[0291] Step 2:

[0292] The server analyzes the data received from the terminal using a natural language processing engine. Specifically, it utilizes NLTK or spaCy in Python to extract important keywords and phrases from the data. The input is the formatted business information, and the output is the analyzed structured data. Based on this data, the subsequent business guideline generation process is initiated.

[0293] Step 3:

[0294] The server inputs the extracted information as prompts into a generation AI model, which then automatically generates business guidelines. Specifically, it uses OpenAI's GPT and outputs the generated business guidelines in text or PDF format. The input is structured data, and the output is a document containing business guidelines.

[0295] Step 4:

[0296] Users review the generated operational guidelines on their terminals. Specifically, they verify the content of the guidelines and input necessary corrections or improvement requests as feedback. The input is the operational guideline document, and the output is the feedback information.

[0297] Step 5:

[0298] The server receives user feedback and updates the operational guidelines. Machine learning algorithms are used to analyze the feedback and improve the guidelines. The input is feedback information, and the output is the updated operational guidelines. This update process refines the guidelines over time.

[0299] Step 6:

[0300] The server stores the updated business guidelines in a centralized management database. Specifically, this involves using SQL or NoSQL databases to establish a structure for storing, accessing, and sharing data. The input is the updated business guidelines, and the output is storage in the knowledge base and information sharing within the organization.

[0301] (Application Example 1)

[0302] 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."

[0303] In modern manufacturing, the management of business information, the optimization of work procedures, and the improvement of product quality are required. In particular, the development of an automated system capable of handling complex business protocols has become an issue. However, in conventional systems, information acquisition, analysis, and improvement through feedback have not been sufficiently carried out, and there have been restrictions on efficient work operation and information sharing between departments.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for acquiring business information, means for analyzing the acquired business information to automatically generate work procedures, means for updating the generated procedure manual based on the feedback from the user, means for storing and sharing the updated procedure manual in the knowledge record, means for learning and improving the analysis model using the user's feedback, and means for the working machine to execute the work procedure based on the product data and business protocol. Thereby, optimization of the business process and rapid information sharing become possible.

[0306] "Business information" refers to all data related to the operation of a company or organization, and is detailed information related to work procedures and product specifications.

[0307] "Work procedure" is a record indicating the specific processes and operations necessary to perform a specific task, and is organized to improve work efficiency.

[0308] "User" refers to an individual or team that conducts business using this system, and is an entity that contributes to improving the accuracy of the system through feedback.

[0309] "Knowledge record" refers to a database or archive for the purpose of sharing information inside and outside the organization, and serves to store and make the accumulated knowledge accessible.

[0310] "Feedback" refers to suggestions for improvement and opinions based on work performance provided by users, which are then used to improve the system's functionality.

[0311] "Working machinery" refers to hardware devices used to automate and perform specific manufacturing or processing tasks.

[0312] A "business protocol" refers to a set of instructions that define standardized procedures and rules necessary for carrying out business operations.

[0313] An "analysis model" refers to an algorithm or framework that processes business information using a computer program and creates products based on the analysis results.

[0314] The system for implementing this invention mainly consists of a server, a terminal, and a work machine. The server acquires business information and utilizes Python libraries such as NLTK and Spacy to analyze the data using natural language processing technology. The analyzed data is used to automatically generate work procedures using a generative AI model that utilizes TensorFlow and PyTorch.

[0315] The terminal is a device that receives input and feedback from users and sends the feedback information to the server. The server updates the work procedures based on the feedback, saves the results in a knowledge record, and organizes it in a format that can be shared within the company. This facilitates information sharing within the organization and contributes to the optimization of subsequent business processes.

[0316] The machines operate based on automatically generated work procedures received from the server, and are responsible for performing physical manufacturing tasks. This leads to increased efficiency in business processes and improved product quality.

[0317] As a concrete example, consider a scenario where a new bread manufacturing process is introduced in a factory. Users input new product specifications and operational protocols via a terminal and send them to a server. The server analyzes the information and sends automatically generated work procedures to the machines. The machines then begin the manufacturing process according to these procedures, and the procedures are improved in real time based on user feedback.

[0318] An example of a prompt for a generative AI model is: "Analyze the user's input data and generate a new dough processing procedure for bread making. The feedback was to shorten the mixing time, but please adjust it to maintain quality."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] Users input business information using a terminal and send it to the server. This input information includes product specifications and business protocols. This input data is preprocessed on the server. For example, it is converted into a data format that facilitates analysis through normalization and tokenization of text data.

[0322] Step 2:

[0323] The server analyzes pre-processed business information and extracts necessary information using natural language processing techniques. This process includes named entity recognition and dependency analysis using NLTK and Spacy. The extracted information is input into a generative AI model. The generative AI model automatically generates work procedures based on the input data and sends the results to the next process.

[0324] Step 3:

[0325] The server receives the generated work procedure and sends it to the terminal. The user reviews the work procedure on the terminal and provides feedback as needed. Examples of feedback include suggestions for improving work efficiency and points to adjust the procedure. Feedback from the user is then entered.

[0326] Step 4:

[0327] Upon receiving feedback, the server updates the work procedure using the generated AI model. The updated procedure is an improved version that reflects user feedback. This improvement process involves continuous learning, improving the model's accuracy. The updated procedure is also saved in the knowledge log.

[0328] Step 5:

[0329] The server transmits updated work instructions to the machine. The machine performs the physical tasks according to the instructions. The performed tasks are required to be efficient in the product manufacturing process. Optimized procedures are used in this implementation.

[0330] 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.

[0331] This invention aims to more effectively manage business processes and generate and improve business procedures that take into account the emotional state of users. Specific embodiments of this system are described below.

[0332] System Configuration

[0333] This system primarily consists of a server, users, terminals, and an emotion engine. Users send work-related documents to the server via their terminals. The server uses a generative AI model to analyze the work data and automatically generate work procedure manuals. The emotion engine also analyzes the user's emotions and utilizes this information in the generation process.

[0334] Analysis of emotional data

[0335] The terminal captures the user's facial expressions and tone of voice through emotion recognition technology when the user views the work procedure manual. The server analyzes this emotion data to evaluate how the user feels about the manual. The evaluation results are used to optimize the content and expression of the manual.

[0336] Generating and updating procedure manuals

[0337] Based on information from the emotion engine, the server fine-tunes the instructions to ensure users can easily understand them. When user feedback is received, the server updates the instructions, and the emotion engine re-evaluates the user's stress level and satisfaction.

[0338] Reflection in the knowledge base

[0339] The updated procedure manuals are stored in the knowledge base. This facilitates information sharing between departments and project teams. Furthermore, the knowledge base contributes to the standardization and efficiency of work procedures.

[0340] Specific example

[0341] For example, when a customer support team reviews their procedures, the emotion engine detects when a user feels frustrated or confused while reading the manual. The server then rephrases the manual's explanation in a more understandable way and adds relevant multimedia content based on the detected emotion. As a result, support staff can perform their duties with reduced stress, and the quality of support improves.

[0342] In this way, by integrating and managing business data and user sentiment data, and realizing a dynamic and user-centered business procedure management system, it is possible to aim for improved operational efficiency and user experience across the entire organization.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The user uploads work-related documents to the terminal. The terminal then sends these documents to the server and prepares to begin data processing.

[0346] Step 2:

[0347] The server applies an AI model to analyze the received business data. This model uses natural language processing techniques to analyze documents and identify business flows and procedures. The analysis results are stored in a template format.

[0348] Step 3:

[0349] The terminal collects user emotional data through an emotion engine while the user is viewing work procedure manuals. This process infers stress levels and satisfaction levels from the user's facial expressions, tone of voice, and other factors.

[0350] Step 4:

[0351] The server analyzes data from the emotion engine and reviews the content of the procedure manual. Based on the user's emotions, it adjusts the wording and tone of the procedure and adds supplementary information as needed.

[0352] Step 5:

[0353] Users utilize the generated instructions and provide feedback via their devices as needed. This feedback includes specific comments and suggestions for improvements.

[0354] Step 6:

[0355] The server updates the procedure manual based on data from the feedback and sentiment engine. These updates may include optimizing business processes and refining specific wording.

[0356] Step 7:

[0357] The server stores updated procedures in a knowledge base. This knowledge base is used for information sharing within the company and is accessible to other employees and departments.

[0358] Step 8:

[0359] The server uses an emotion engine to improve its analysis model. It works with feedback data to train the model to improve the accuracy of generating and updating business procedures.

[0360] (Example 2)

[0361] 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".

[0362] While documenting and standardizing business procedures contributes to improved organizational productivity, traditional methods struggle to consider users' emotional states, resulting in procedural documents that are difficult for users to understand. Furthermore, efficient evaluation and updating methods are needed when utilizing user feedback to improve procedures. Additionally, a system is required to share updated information across the entire organization.

[0363] 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.

[0364] In this invention, the server includes means for acquiring business information, means for sensing the user's facial expressions and tone of voice to collect emotional states, and means for extracting key points of business procedures and proposing optimizations using a generative AI model. This enables the generation of business procedure manuals that take user emotions into consideration, as well as the sharing and updating of information across the entire organization.

[0365] "Business information" refers to all data related to business operations performed within an organization, including procedures, records, input data, and user feedback.

[0366] A "procedure document" is a document that describes the steps and procedures necessary to carry out a task, and its purpose is to improve work efficiency and standardize operations.

[0367] "User facial expressions and tone of voice" refer to the nonverbal expressions and vocal characteristics that users exhibit when using business documents, and are used to estimate the user's emotional state.

[0368] "Emotional information" refers to data about a user's emotional responses and psychological state, obtained through analysis of facial expressions and tone of voice.

[0369] A "generative AI model" is a machine learning model that uses advanced artificial intelligence technologies to analyze data and automatically generate and update procedural documents.

[0370] "To propose an optimization" means to find the most efficient or effective method or means under specific conditions and present it in a feasible form.

[0371] A "knowledge base" is a database system that centrally manages and stores business procedures and related information, enabling information sharing within an organization.

[0372] The specific configuration and operation of this system are shown to illustrate embodiments for carrying out the invention.

[0373] This system aims to generate and optimize business procedure manuals and consists mainly of a server, terminals, and users. The server is equipped with a high-performance processor and sufficient memory, and a generative AI model runs on it to analyze business data. The generative AI model receives business information provided by users and automatically generates procedure documents based on that data. Furthermore, the model optimizes the document content by incorporating emotional information into the model.

[0374] The device has software installed that detects the user's facial expressions and tone of voice, and is equipped with the ability to capture this data through the camera and microphone. The device sends this data to a server, which analyzes it and evaluates the user's emotional state. This makes it possible to generate procedural documents that take the user's emotions into consideration.

[0375] For example, if a user is using a new product's shipping procedure manual and encounters an unclear step, causing them to show confusion, the terminal will detect this expression. The server will then use this emotional information to create a prompt instructing the generative AI model to "update the steps to be concise and clear," thereby revising the procedure manual. In this way, user feedback is reflected in the procedure manual in real time.

[0376] As a concrete example, a prompt could be issued to the generating AI model with the instruction, "Update the product shipping procedure manual to be concise and clear, and make improvements based on sentiment data." Based on this instruction, the server analyzes business information and improves the procedure document.

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] Users prepare work-related documents via their terminals and send them to the server. The input consists of work-related information, which may include existing procedures and feedback. Specifically, the user selects a document and presses the send button.

[0380] Step 2:

[0381] The server performs data analysis by inputting received business information into a generating AI model. The model analyzes the business information, extracts the key points of the procedure manual, and generates an initial procedure document. The output is the generated procedure document. Specifically, the model summarizes the text and organizes the procedures.

[0382] Step 3:

[0383] The device uses its camera and microphone to capture facial expressions and voice tone as the user views generated procedure documents. Input is non-verbal data from the user, and output is sent to the server as emotional information. The device captures information in real time.

[0384] Step 4:

[0385] The server analyzes emotional information transmitted from the terminal and evaluates the user's emotional state. The analysis reveals the user's psychological response to the procedural document. The input is emotional information, and the output is the user's emotional evaluation. Specifically, the server uses an emotional recognition algorithm.

[0386] Step 5:

[0387] The server sends prompts to the generative AI model based on sentiment evaluation, and adjusts the procedure document. For example, if the user shows a confused reaction, it sends a prompt to "simplify the steps." The input is the prompt, and the output is the updated procedure document. Specifically, the server highlights key points and adds explanations.

[0388] Step 6:

[0389] The updated procedure document is stored in the knowledge base and shared within the organization. The input is the updated procedure document, and the output is the addition of an entry to the knowledge base. The server sends the data to the knowledge management system, and the information is stored.

[0390] This series of steps ensures that operational procedures are always kept up-to-date and user-friendly, and enables efficient information sharing throughout the organization.

[0391] (Application Example 2)

[0392] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0393] Traditional business procedure systems have struggled to provide dynamic procedures that take into account user emotions and states, resulting in limited improvements to the user experience. This can lead to decreased operational efficiency and lower customer satisfaction, highlighting the need for adaptive and user-centered procedure management systems.

[0394] 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.

[0395] In this invention, the server includes means for acquiring business data, means for analyzing the acquired business data to automatically generate business procedures, means for updating the generated procedure manuals based on user feedback, means for analyzing an individual's emotional state using emotion recognition technology, and means for adaptively changing the business procedures based on the analyzed emotional state. This makes it possible to reflect the user's emotional state in real time and provide optimized procedure manuals.

[0396] "Business data" refers to data that includes information related to work and transactions, and serves as basic information for systems to process and analyze.

[0397] "Business procedures" are a set of steps and instructions that outline how to perform a specific task, serving as a guideline for efficient and effective work execution.

[0398] A "procedure manual" is a document that describes in detail the steps and methods for performing a task, serving as a guideline to help employees understand their work.

[0399] A "knowledge base" is a database that stores information and procedures related to business operations, and is used for sharing and reusing them throughout the organization.

[0400] "Emotion recognition technology" is a technology that analyzes an individual's facial expressions, voice, and behavior to determine their emotions, and is a means for computers to understand human emotions.

[0401] "Emotional state" refers to the state in which an individual exhibits psychological and physiological responses in a particular situation, and is a factor that significantly influences the user experience.

[0402] "Adaptive modification" refers to a method where the system automatically adjusts and updates itself in response to changes in circumstances and users, thereby achieving optimal results.

[0403] The system implementing this invention mainly consists of a server, an emotion recognition terminal, and a user. The server receives business data and automatically generates business procedures using a generative AI model. The emotion recognition terminal analyzes the user's facial expressions and tone of voice as they use the procedure manual and evaluates their emotions in real time. The server takes in the user's emotional state and feedback and dynamically adjusts the business procedures based on this information.

[0404] Specifically, the emotion recognition terminal uses smart glasses, allowing users to perform tasks while visually obtaining information. The software implements EmotionAPI and real-time voice tone analysis as emotion recognition technologies, thereby precisely determining the user's emotions. The generative AI model uses the latest natural language processing technology to provide work procedures that respond to user feedback and emotional states.

[0405] For example, when introducing a new product in a physical store, if emotion recognition technology detects that the user is confused, the server can use a generative AI model to generate a "prompt message to provide a clearer explanation" and display it through the emotion recognition terminal. This allows the sales staff to quickly provide appropriate explanations to capture the customer's attention, thereby improving the customer experience.

[0406] An example of a prompt for the generating AI model would be, "Provide a simplified explanation for a customer who seems confused about the new product features," which helps generate appropriate guidance based on the user's situation.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The device captures the user's facial expressions and voice tone in real time. It takes raw data from the camera and microphone as input. This data is analyzed using emotion recognition technology to output the user's emotional state (e.g., satisfaction, confusion, anxiety). Specifically, it uses the EmotionAPI to identify emotions from facial muscle movements and voice intonation.

[0410] Step 2:

[0411] The server automatically generates appropriate work procedure manuals using a generative AI model based on acquired emotional state data. The input consists of the user's emotional state and related work data. The generative AI model analyzes this data to generate optimal explanations and customer service procedures. The output is a procedure manual that is easy for the user to understand. The system generates prompt statements based on the emotional data, and the content corresponding to these prompts is reflected in the procedure manual.

[0412] Step 3:

[0413] The terminal displays the generated procedure manual, allowing the user to visually confirm it. The input is the content of the procedure manual sent from the server. As output, the procedure manual is displayed on a screen such as smart glasses in a user-friendly format. Specifically, the procedure manual, including supplementary explanations tailored to the user's emotions, is presented on the screen.

[0414] Step 4:

[0415] Users perform tasks based on the displayed instructions and input feedback into the terminal as needed. This feedback is sent to the server and used to improve future instructions. Input methods include feedback forms and voice input, and output is points for improvement in the instructions. Operationally, user feedback is recorded in a digital form and stored on the server.

[0416] Step 5:

[0417] The server analyzes user feedback and compares it with emotional states to improve future procedures. Inputs are past emotional data and feedback data. Output is an updated analysis model. Specifically, a generative AI model learns from the feedback and works to generate more effective prompts.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] This invention is a system that effectively manages various business-related data and automatically generates and updates business procedures. This system primarily consists of three elements: a server, users, and terminals. Specific embodiments are described below.

[0435] System Configuration

[0436] Users input work-related documents using a terminal and send them to the server. The server analyzes the acquired work data and automatically generates work procedures using an AI model. In this process, the server processes the data using natural language and generates procedure manuals based on the analyzed information. The generated procedure manuals are then continuously improved through user feedback.

[0437] Update of the procedure manual

[0438] When a user submits feedback from their device, the server receives the information and incorporates it into the procedure manual. The server updates the procedure manual in real time and incorporates new business data as needed.

[0439] Knowledge-based management

[0440] Updated procedures are stored in a central knowledge base. This allows the server to share information across departments. Furthermore, the stored information is arranged to be easily accessible to other departments and relevant teams.

[0441] Learning and Improvement

[0442] User feedback is used as training data for the analysis model. The server updates the model based on this data, improving the accuracy of generating business procedures. This leads to improved operational efficiency across the organization and enhanced knowledge sharing.

[0443] Specific example

[0444] Consider a manufacturing company where production procedures are required when launching a new product line. The user inputs product specifications and protocols into a terminal and sends them to a server. The server analyzes this data and automatically generates step-by-step manufacturing procedures. The user reviews these procedures and provides feedback from the factory floor. The server incorporates the feedback, updates the procedures, and stores them in a knowledge base. This allows other project teams within the company to access the information, leading to standardization and increased efficiency in operations.

[0445] In this way, by systematically integrating everything from the analysis of business data to the automation of procedures and the update process, a system is built that promotes organizational knowledge management and improves operational efficiency.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] Users prepare work-related documents and data files on their terminals and upload them to the system. This data may include requirements specifications, work procedures, and past work performance data.

[0449] Step 2:

[0450] The terminal transfers the uploaded data to the server. The server confirms that it has successfully received the data and then proceeds to the next analysis process.

[0451] Step 3:

[0452] The server analyzes the received business data using a generative AI model. Using natural language processing techniques, it extracts business procedures, related tasks, and points to note from each document. Based on this information, it constructs a preliminary model of the business procedures.

[0453] Step 4:

[0454] The server automatically generates procedure manuals in template format based on the extracted information. These manuals include key steps, detailed explanations, and, if necessary, multimedia content.

[0455] Step 5:

[0456] Users review the generated procedure manuals and provide feedback on areas that need improvement or additional information. In this process, users send comments and revision requests to the server using their terminals.

[0457] Step 6:

[0458] The server analyzes the feedback received from users and incorporates it into the procedure manual. If necessary, it adjusts the work procedures and content, updating the manual.

[0459] Step 7:

[0460] The server stores updated procedures in a knowledge base. This knowledge base is shared among departments and project teams and used as an information resource for the entire organization.

[0461] Step 8:

[0462] The server updates the learning algorithm of the generated AI model based on user feedback. Through this learning process, the model's accuracy is improved and reflected in subsequent business data analysis and procedure manual generation.

[0463] (Example 1)

[0464] 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."

[0465] In modern business operations, it is essential to efficiently manage relevant information and to quickly create and update operational guidelines. However, manually creating and updating operational guidelines is time-consuming and labor-intensive, and sharing information between different departments is difficult. Furthermore, there is a need for methods to effectively incorporate user feedback into operational guidelines and improve analytical models.

[0466] 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.

[0467] In this invention, the server includes means for analyzing business-related information using natural language processing technology, means for updating business guidelines based on user feedback, and means for storing and transmitting the updated business guidelines using a centralized management database. This enables efficient business management, rapid information sharing, and continuous improvement of the analysis model.

[0468] "Business-related information" refers to data, documents, specifications, protocols, etc., necessary for carrying out business operations within a corporation or organization.

[0469] "Natural language processing technology" refers to the technology that enables computers to understand and analyze the language that humans use on a daily basis.

[0470] "Business guidelines" refer to documents that describe the procedures and methods for performing a specific task.

[0471] "User feedback" refers to feedback information such as suggestions for improvement or requests for corrections regarding business guidelines and systems.

[0472] A "centralized management database" refers to an information system that centrally stores and manages data within a company or organization, and provides access to it as needed.

[0473] An "analytical model" refers to a mathematical or algorithmic structure used to analyze data and make predictions or decisions based on the results.

[0474] Modes for carrying out the invention

[0475] This invention is a system for efficiently managing various business-related information and for automatically generating and updating business guidelines. Specific embodiments focusing on the server, terminal, and user are described below.

[0476] Initial data entry and transmission

[0477] Users input work-related information through a terminal. This information includes specifications and business protocols. The system is designed to allow intuitive input using a GUI (Graphical User Interface). The terminal sends the entered data to the server. The data is transferred in JSON or XML format via a secure communication protocol.

[0478] Data analysis and generation of operational guidelines

[0479] The server analyzes the received business information using a natural language processing engine (e.g., Python's NLTK or spaCy). This analysis extracts important information necessary for business guidelines. The server then automatically generates business guidelines based on the extracted information using a generative AI model (e.g., GPT). The generated guidelines are output in PDF or text format and provided to the user.

[0480] Gathering feedback and updating guidelines

[0481] Users review the provided operational guidelines and provide feedback from their terminals to the server. This feedback includes specific additions, such as "additional explanations are needed for the assembly steps." The server analyzes the feedback and updates the operational guidelines regularly. The updated guidelines are stored in a centralized management database, enabling immediate information sharing within the organization.

[0482] Knowledge integration and sharing

[0483] The server centralizes updated operational guidelines in a centralized management database to ensure knowledge consolidation. The stored data is designed for easy access by other departments and relevant teams, facilitating efficient information sharing across departments.

[0484] Specific example

[0485] Consider a set of operational guidelines to be used when launching a new product line in a manufacturing company. The user inputs the specifications of the new product into a terminal and sends them to a server. The server uses natural language processing to analyze the input and generate operational guidelines that include specific manufacturing procedures. These guidelines include specific steps such as "Step 1: Material preparation" and "Step 2: Part assembly." If the user sends feedback such as "Additional safety precautions are needed for the part assembly step," the server updates the operational guidelines based on that feedback. By continuing this cycle, the efficiency and quality of operations will improve.

[0486] Example of a prompt

[0487] Please generate manufacturing guidelines for the new product XYZ. The specifications should include the following items: materials, dimensions, and assembly procedure.

[0488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0489] Step 1:

[0490] The user inputs work-related information into the terminal. Specifically, they input specifications and protocols, and the terminal sends this to the server in JSON or XML format. The input data contains detailed information necessary for the work, and the output is the appropriate formatting and transfer of this data to the server.

[0491] Step 2:

[0492] The server analyzes data received from terminals using a natural language processing engine. Specifically, it uses Python's NLTK and spaCy to extract important keywords and phrases from the data. The input is formatted business information, and the output is analyzed structured data. Based on this data, the subsequent business guideline generation process begins.

[0493] Step 3:

[0494] The server inputs the extracted information as prompts into a generation AI model, which then automatically generates business guidelines. Specifically, it uses OpenAI's GPT and outputs the generated business guidelines in text or PDF format. The input is structured data, and the output is a document containing business guidelines.

[0495] Step 4:

[0496] Users review the generated operational guidelines on their terminals. Specifically, they verify the content of the guidelines and input necessary corrections or improvement requests as feedback. The input is the operational guideline document, and the output is the feedback information.

[0497] Step 5:

[0498] The server receives user feedback and updates the operational guidelines. Machine learning algorithms are used to analyze the feedback and improve the guidelines. The input is feedback information, and the output is the updated operational guidelines. This update process refines the guidelines over time.

[0499] Step 6:

[0500] The server stores the updated business guidelines in a centralized management database. Specifically, this involves using SQL or NoSQL databases to establish a structure for storing, accessing, and sharing data. The input is the updated business guidelines, and the output is storage in the knowledge base and information sharing within the organization.

[0501] (Application Example 1)

[0502] 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."

[0503] In modern manufacturing, there is a demand for efficient management of operational information, optimization of work procedures, and improvement of product quality. In particular, developing automated systems capable of handling complex operational protocols is a challenge. However, conventional systems have limited efficient work operations and interdepartmental information sharing due to insufficient information acquisition, analysis, and feedback for improvement.

[0504] 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.

[0505] In this invention, the server includes means for acquiring business information, means for analyzing the acquired business information to automatically generate work procedures, means for updating the generated procedure manuals based on user feedback, means for storing and sharing the updated procedure manuals in a knowledge record, means for learning and improving the analysis model using user feedback, and means for the work machine to execute the work procedures based on product data and business protocols. This enables optimization of business processes and rapid information sharing.

[0506] "Business information" refers to all data related to the operation of a company or organization, including detailed information related to work procedures and product specifications.

[0507] A "work procedure" is a record that shows the specific steps and operations necessary to perform a particular task, and is compiled to improve work efficiency.

[0508] "Users" refer to individuals or teams who utilize this system for their work and contribute to improving the system's accuracy through feedback.

[0509] A "knowledge record" refers to a database or archive intended for sharing information within and outside an organization, preserving and making accessible accumulated knowledge.

[0510] "Feedback" refers to suggestions for improvement and opinions based on work performance provided by users, which are then used to improve the system's functionality.

[0511] "Working machinery" refers to hardware devices used to automate and perform specific manufacturing or processing tasks.

[0512] A "business protocol" refers to a set of instructions that define standardized procedures and rules necessary for carrying out business operations.

[0513] An "analysis model" refers to an algorithm or framework that processes business information using a computer program and creates products based on the analysis results.

[0514] The system for implementing this invention mainly consists of a server, a terminal, and a work machine. The server acquires business information and utilizes Python libraries such as NLTK and Spacy to analyze the data using natural language processing technology. The analyzed data is used to automatically generate work procedures using a generative AI model that utilizes TensorFlow and PyTorch.

[0515] The terminal is a device that receives input and feedback from users and sends the feedback information to the server. The server updates the work procedures based on the feedback, saves the results in a knowledge record, and organizes it in a format that can be shared within the company. This facilitates information sharing within the organization and contributes to the optimization of subsequent business processes.

[0516] The machines operate based on automatically generated work procedures received from the server, and are responsible for performing physical manufacturing tasks. This leads to increased efficiency in business processes and improved product quality.

[0517] As a concrete example, consider a scenario where a new bread manufacturing process is introduced in a factory. Users input new product specifications and operational protocols via a terminal and send them to a server. The server analyzes the information and sends automatically generated work procedures to the machines. The machines then begin the manufacturing process according to these procedures, and the procedures are improved in real time based on user feedback.

[0518] An example of a prompt for a generative AI model is: "Analyze the user's input data and generate a new dough processing procedure for bread making. The feedback was to shorten the mixing time, but please adjust it to maintain quality."

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] Users input business information using a terminal and send it to the server. This input information includes product specifications and business protocols. This input data is preprocessed on the server. For example, it is converted into a data format that facilitates analysis through normalization and tokenization of text data.

[0522] Step 2:

[0523] The server analyzes pre-processed business information and extracts necessary information using natural language processing techniques. This process includes named entity recognition and dependency analysis using NLTK and Spacy. The extracted information is input into a generative AI model. The generative AI model automatically generates work procedures based on the input data and sends the results to the next process.

[0524] Step 3:

[0525] The server receives the generated work procedure and sends it to the terminal. The user reviews the work procedure on the terminal and provides feedback as needed. Examples of feedback include suggestions for improving work efficiency and points to adjust the procedure. Feedback from the user is then entered.

[0526] Step 4:

[0527] Upon receiving feedback, the server updates the work procedure using the generated AI model. The updated procedure is an improved version that reflects user feedback. This improvement process involves continuous learning, improving the model's accuracy. The updated procedure is also saved in the knowledge log.

[0528] Step 5:

[0529] The server transmits updated work instructions to the machine. The machine performs the physical tasks according to the instructions. The performed tasks are required to be efficient in the product manufacturing process. Optimized procedures are used in this implementation.

[0530] 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.

[0531] This invention aims to more effectively manage business processes and generate and improve business procedures that take into account the emotional state of users. Specific embodiments of this system are described below.

[0532] System Configuration

[0533] This system primarily consists of a server, users, terminals, and an emotion engine. Users send work-related documents to the server via their terminals. The server uses a generative AI model to analyze the work data and automatically generate work procedure manuals. The emotion engine also analyzes the user's emotions and utilizes this information in the generation process.

[0534] Analysis of emotional data

[0535] The terminal captures the user's facial expressions and tone of voice through emotion recognition technology when the user views the work procedure manual. The server analyzes this emotion data to evaluate how the user feels about the manual. The evaluation results are used to optimize the content and expression of the manual.

[0536] Generating and updating procedure manuals

[0537] Based on information from the emotion engine, the server fine-tunes the instructions to ensure users can easily understand them. When user feedback is received, the server updates the instructions, and the emotion engine re-evaluates the user's stress level and satisfaction.

[0538] Reflection in the knowledge base

[0539] The updated procedure manuals are stored in the knowledge base. This facilitates information sharing between departments and project teams. Furthermore, the knowledge base contributes to the standardization and efficiency of work procedures.

[0540] Specific example

[0541] For example, when a customer support team reviews their procedures, the emotion engine detects when a user feels frustrated or confused while reading the manual. The server then rephrases the manual's explanation in a more understandable way and adds relevant multimedia content based on the detected emotion. As a result, support staff can perform their duties with reduced stress, and the quality of support improves.

[0542] In this way, by integrating and managing business data and user sentiment data, and realizing a dynamic and user-centered business procedure management system, it is possible to aim for improved operational efficiency and user experience across the entire organization.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The user uploads work-related documents to the terminal. The terminal then sends these documents to the server and prepares to begin data processing.

[0546] Step 2:

[0547] The server applies an AI model to analyze the received business data. This model uses natural language processing techniques to analyze documents and identify business flows and procedures. The analysis results are stored in a template format.

[0548] Step 3:

[0549] The terminal collects user emotional data through an emotion engine while the user is viewing work procedure manuals. This process infers stress levels and satisfaction levels from the user's facial expressions, tone of voice, and other factors.

[0550] Step 4:

[0551] The server analyzes data from the emotion engine and reviews the content of the procedure manual. Based on the user's emotions, it adjusts the wording and tone of the procedure and adds supplementary information as needed.

[0552] Step 5:

[0553] Users utilize the generated instructions and provide feedback via their devices as needed. This feedback includes specific comments and suggestions for improvements.

[0554] Step 6:

[0555] The server updates the procedure manual based on data from the feedback and sentiment engine. These updates may include optimizing business processes and refining specific wording.

[0556] Step 7:

[0557] The server stores updated procedures in a knowledge base. This knowledge base is used for information sharing within the company and is accessible to other employees and departments.

[0558] Step 8:

[0559] The server uses an emotion engine to improve its analysis model. It works with feedback data to train the model to improve the accuracy of generating and updating business procedures.

[0560] (Example 2)

[0561] 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."

[0562] While documenting and standardizing business procedures contributes to improved organizational productivity, traditional methods struggle to consider users' emotional states, resulting in procedural documents that are difficult for users to understand. Furthermore, efficient evaluation and updating methods are needed when utilizing user feedback to improve procedures. Additionally, a system is required to share updated information across the entire organization.

[0563] 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.

[0564] In this invention, the server includes means for acquiring business information, means for sensing the user's facial expressions and tone of voice to collect emotional states, and means for extracting key points of business procedures and proposing optimizations using a generative AI model. This enables the generation of business procedure manuals that take user emotions into consideration, as well as the sharing and updating of information across the entire organization.

[0565] "Business information" refers to all data related to business operations performed within an organization, including procedures, records, input data, and user feedback.

[0566] A "procedure document" is a document that describes the steps and procedures necessary to carry out a task, and its purpose is to improve work efficiency and standardize operations.

[0567] "User facial expressions and tone of voice" refer to the nonverbal expressions and vocal characteristics that users exhibit when using business documents, and are used to estimate the user's emotional state.

[0568] "Emotional information" refers to data about a user's emotional responses and psychological state, obtained through analysis of facial expressions and tone of voice.

[0569] A "generative AI model" is a machine learning model that uses advanced artificial intelligence technologies to analyze data and automatically generate and update procedural documents.

[0570] "To propose an optimization" means to find the most efficient or effective method or means under specific conditions and present it in a feasible form.

[0571] A "knowledge base" is a database system that centrally manages and stores business procedures and related information, enabling information sharing within an organization.

[0572] The specific configuration and operation of this system are shown to illustrate embodiments for carrying out the invention.

[0573] This system aims to generate and optimize business procedure manuals and consists mainly of a server, terminals, and users. The server is equipped with a high-performance processor and sufficient memory, and a generative AI model runs on it to analyze business data. The generative AI model receives business information provided by users and automatically generates procedure documents based on that data. Furthermore, the model optimizes the document content by incorporating emotional information into the model.

[0574] The device has software installed that detects the user's facial expressions and tone of voice, and is equipped with the ability to capture this data through the camera and microphone. The device sends this data to a server, which analyzes it and evaluates the user's emotional state. This makes it possible to generate procedural documents that take the user's emotions into consideration.

[0575] For example, if a user is using a new product's shipping procedure manual and encounters an unclear step, causing them to show confusion, the terminal will detect this expression. The server will then use this emotional information to create a prompt instructing the generative AI model to "update the steps to be concise and clear," thereby revising the procedure manual. In this way, user feedback is reflected in the procedure manual in real time.

[0576] As a concrete example, a prompt could be issued to the generating AI model with the instruction, "Update the product shipping procedure manual to be concise and clear, and make improvements based on sentiment data." Based on this instruction, the server analyzes business information and improves the procedure document.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] Users prepare work-related documents via their terminals and send them to the server. The input consists of work-related information, which may include existing procedures and feedback. Specifically, the user selects a document and presses the send button.

[0580] Step 2:

[0581] The server performs data analysis by inputting received business information into a generating AI model. The model analyzes the business information, extracts the key points of the procedure manual, and generates an initial procedure document. The output is the generated procedure document. Specifically, the model summarizes the text and organizes the procedures.

[0582] Step 3:

[0583] The device uses its camera and microphone to capture facial expressions and voice tone as the user views generated procedure documents. Input is non-verbal data from the user, and output is sent to the server as emotional information. The device captures information in real time.

[0584] Step 4:

[0585] The server analyzes emotional information transmitted from the terminal and evaluates the user's emotional state. The analysis reveals the user's psychological response to the procedural document. The input is emotional information, and the output is the user's emotional evaluation. Specifically, the server uses an emotional recognition algorithm.

[0586] Step 5:

[0587] The server sends prompts to the generative AI model based on sentiment evaluation, and adjusts the procedure document. For example, if the user shows a confused reaction, it sends a prompt to "simplify the steps." The input is the prompt, and the output is the updated procedure document. Specifically, the server highlights key points and adds explanations.

[0588] Step 6:

[0589] The updated procedure document is stored in the knowledge base and shared within the organization. The input is the updated procedure document, and the output is the addition of an entry to the knowledge base. The server sends the data to the knowledge management system, and the information is stored.

[0590] This series of steps ensures that operational procedures are always kept up-to-date and user-friendly, and enables efficient information sharing throughout the organization.

[0591] (Application Example 2)

[0592] 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."

[0593] Traditional business procedure systems have struggled to provide dynamic procedures that take into account user emotions and states, resulting in limited improvements to the user experience. This can lead to decreased operational efficiency and lower customer satisfaction, highlighting the need for adaptive and user-centered procedure management systems.

[0594] 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.

[0595] In this invention, the server includes means for acquiring business data, means for analyzing the acquired business data to automatically generate business procedures, means for updating the generated procedure manuals based on user feedback, means for analyzing an individual's emotional state using emotion recognition technology, and means for adaptively changing the business procedures based on the analyzed emotional state. This makes it possible to reflect the user's emotional state in real time and provide optimized procedure manuals.

[0596] "Business data" refers to data that includes information related to work and transactions, and serves as basic information for systems to process and analyze.

[0597] "Business procedures" are a set of steps and instructions that outline how to perform a specific task, serving as a guideline for efficient and effective work execution.

[0598] A "procedure manual" is a document that describes in detail the steps and methods for performing a task, serving as a guideline to help employees understand their work.

[0599] A "knowledge base" is a database that stores information and procedures related to business operations, and is used for sharing and reusing them throughout the organization.

[0600] "Emotion recognition technology" is a technology that analyzes an individual's facial expressions, voice, and behavior to determine their emotions, and is a means for computers to understand human emotions.

[0601] "Emotional state" refers to the state in which an individual exhibits psychological and physiological responses in a particular situation, and is a factor that significantly influences the user experience.

[0602] "Adaptive modification" refers to a method where the system automatically adjusts and updates itself in response to changes in circumstances and users, thereby achieving optimal results.

[0603] The system implementing this invention mainly consists of a server, an emotion recognition terminal, and a user. The server receives business data and automatically generates business procedures using a generative AI model. The emotion recognition terminal analyzes the user's facial expressions and tone of voice as they use the procedure manual and evaluates their emotions in real time. The server takes in the user's emotional state and feedback and dynamically adjusts the business procedures based on this information.

[0604] Specifically, the emotion recognition terminal uses smart glasses, allowing users to perform tasks while visually obtaining information. The software implements EmotionAPI and real-time voice tone analysis as emotion recognition technologies, thereby precisely determining the user's emotions. The generative AI model uses the latest natural language processing technology to provide work procedures that respond to user feedback and emotional states.

[0605] For example, when introducing a new product in a physical store, if emotion recognition technology detects that the user is confused, the server can use a generative AI model to generate a "prompt message to provide a clearer explanation" and display it through the emotion recognition terminal. This allows the sales staff to quickly provide appropriate explanations to capture the customer's attention, thereby improving the customer experience.

[0606] An example of a prompt for the generating AI model would be, "Provide a simplified explanation for a customer who seems confused about the new product features," which helps generate appropriate guidance based on the user's situation.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The device captures the user's facial expressions and voice tone in real time. It takes raw data from the camera and microphone as input. This data is analyzed using emotion recognition technology to output the user's emotional state (e.g., satisfaction, confusion, anxiety). Specifically, it uses the EmotionAPI to identify emotions from facial muscle movements and voice intonation.

[0610] Step 2:

[0611] The server automatically generates appropriate work procedure manuals using a generative AI model based on acquired emotional state data. The input consists of the user's emotional state and related work data. The generative AI model analyzes this data to generate optimal explanations and customer service procedures. The output is a procedure manual that is easy for the user to understand. The system generates prompt statements based on the emotional data, and the content corresponding to these prompts is reflected in the procedure manual.

[0612] Step 3:

[0613] The terminal displays the generated procedure manual, allowing the user to visually confirm it. The input is the content of the procedure manual sent from the server. As output, the procedure manual is displayed on a screen such as smart glasses in a user-friendly format. Specifically, the procedure manual, including supplementary explanations tailored to the user's emotions, is presented on the screen.

[0614] Step 4:

[0615] Users perform tasks based on the displayed instructions and input feedback into the terminal as needed. This feedback is sent to the server and used to improve future instructions. Input methods include feedback forms and voice input, and output is points for improvement in the instructions. Operationally, user feedback is recorded in a digital form and stored on the server.

[0616] Step 5:

[0617] The server analyzes user feedback and compares it with emotional states to improve future procedures. Inputs are past emotional data and feedback data. Output is an updated analysis model. Specifically, a generative AI model learns from the feedback and works to generate more effective prompts.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention is a system that effectively manages various business-related data and automatically generates and updates business procedures. This system primarily consists of three elements: a server, users, and terminals. Specific embodiments are described below.

[0636] System Configuration

[0637] Users input work-related documents using a terminal and send them to the server. The server analyzes the acquired work data and automatically generates work procedures using an AI model. In this process, the server processes the data using natural language and generates procedure manuals based on the analyzed information. The generated procedure manuals are then continuously improved through user feedback.

[0638] Update of the procedure manual

[0639] When a user sends feedback from their device, the server receives the information and incorporates it into the procedure manual. The server updates the procedure manual in real time and incorporates new business data as needed.

[0640] Knowledge-based management

[0641] Updated procedures are stored in a central knowledge base. This allows the server to share information across departments. Furthermore, the stored information is arranged to be easily accessible to other departments and relevant teams.

[0642] Learning and Improvement

[0643] User feedback is used as training data for the analysis model. The server updates the model based on this data, improving the accuracy of generating business procedures. This leads to improved operational efficiency across the organization and enhanced knowledge sharing.

[0644] Specific example

[0645] Consider a manufacturing company where production procedures are required when launching a new product line. The user inputs product specifications and protocols into a terminal and sends them to a server. The server analyzes this data and automatically generates step-by-step manufacturing procedures. The user reviews these procedures and provides feedback from the factory floor. The server incorporates the feedback, updates the procedures, and stores them in a knowledge base. This allows other project teams within the company to access the information, leading to standardization and increased efficiency in operations.

[0646] In this way, by systematically integrating everything from the analysis of business data to the automation of procedures and the update process, a system is built that promotes organizational knowledge management and improves operational efficiency.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] Users prepare work-related documents and data files on their terminals and upload them to the system. This data may include requirements specifications, work procedures, and past work performance data.

[0650] Step 2:

[0651] The terminal transfers the uploaded data to the server. The server confirms that it has successfully received the data and then proceeds to the next analysis process.

[0652] Step 3:

[0653] The server analyzes the received business data using a generative AI model. Using natural language processing techniques, it extracts business procedures, related tasks, and points to note from each document. Based on this information, it constructs a preliminary model of the business procedures.

[0654] Step 4:

[0655] The server automatically generates procedure manuals in template format based on the extracted information. These manuals include key steps, detailed explanations, and, if necessary, multimedia content.

[0656] Step 5:

[0657] Users review the generated procedure manuals and provide feedback on areas that need improvement or additional information. In this process, users send comments and revision requests to the server using their terminals.

[0658] Step 6:

[0659] The server analyzes the feedback received from users and incorporates it into the procedure manual. If necessary, it adjusts the work procedures and content, updating the manual.

[0660] Step 7:

[0661] The server stores updated procedures in a knowledge base. This knowledge base is shared among departments and project teams and used as an information resource for the entire organization.

[0662] Step 8:

[0663] The server updates the learning algorithm of the generated AI model based on user feedback. Through learning, the model's accuracy is improved and reflected in subsequent business data analysis and procedure manual generation.

[0664] (Example 1)

[0665] 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".

[0666] In modern business operations, there is a need to efficiently manage relevant information and to quickly create and update operational guidelines. However, manually creating and updating operational guidelines is time-consuming and labor-intensive, and sharing information between different departments is difficult. Furthermore, there is a need for methods to effectively incorporate user feedback into operational guidelines and improve analytical models.

[0667] 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.

[0668] In this invention, the server includes means for analyzing business-related information using natural language processing technology, means for updating business guidelines based on user feedback, and means for storing and transmitting the updated business guidelines using a centralized management database. This enables efficient business management, rapid information sharing, and continuous improvement of the analysis model.

[0669] "Business-related information" refers to data, documents, specifications, protocols, etc., necessary for carrying out business operations within a corporation or organization.

[0670] "Natural language processing technology" refers to the technology that enables computers to understand and analyze the language that humans use on a daily basis.

[0671] "Business guidelines" refer to documents that describe the procedures and methods for performing a specific task.

[0672] "User feedback" refers to feedback information such as suggestions for improvement or requests for corrections regarding business guidelines and systems.

[0673] A "centralized management database" refers to an information system that centrally stores and manages data within a company or organization, and provides access to it as needed.

[0674] An "analytical model" refers to a mathematical or algorithmic structure used to analyze data and make predictions or decisions based on the results.

[0675] Modes for carrying out the invention

[0676] This invention is a system for efficiently managing various business-related information and for automatically generating and updating business guidelines. Specific embodiments focusing on the server, terminal, and user are described below.

[0677] Initial data entry and transmission

[0678] Users input work-related information through a terminal. This information includes specifications and business protocols. The system is designed to allow intuitive input using a GUI (Graphical User Interface). The terminal sends the entered data to the server. The data is transferred in JSON or XML format via a secure communication protocol.

[0679] Data analysis and generation of operational guidelines

[0680] The server analyzes the received business information using a natural language processing engine (e.g., Python's NLTK or spaCy). This analysis extracts important information necessary for business guidelines. The server then automatically generates business guidelines based on the extracted information using a generative AI model (e.g., GPT). The generated guidelines are output in PDF or text format and provided to the user.

[0681] Gathering feedback and updating guidelines

[0682] Users review the provided operational guidelines and provide feedback from their terminals to the server. This feedback includes specific additions, such as "additional explanations are needed for the assembly steps." The server analyzes the feedback and updates the operational guidelines regularly. The updated guidelines are stored in a centralized management database, enabling immediate information sharing within the organization.

[0683] Knowledge integration and sharing

[0684] The server centralizes updated operational guidelines by storing them in a centralized management database to centralize knowledge. The stored data is designed to be easily accessible to other departments and relevant teams, thus facilitating efficient information sharing across departments.

[0685] Specific example

[0686] Consider a set of operational guidelines to be used when launching a new product line in a manufacturing company. The user inputs the specifications of the new product into a terminal and sends them to a server. The server uses natural language processing to analyze the input and generate operational guidelines that include specific manufacturing procedures. These guidelines include specific steps such as "Step 1: Material preparation" and "Step 2: Part assembly." If the user sends feedback such as "Additional safety precautions are needed for the part assembly step," the server updates the operational guidelines based on that feedback. By continuing this cycle, the efficiency and quality of operations will improve.

[0687] Example of a prompt

[0688] Please generate manufacturing guidelines for the new product XYZ. The specifications should include the following items: materials, dimensions, and assembly procedure.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user inputs work-related information into the terminal. Specifically, they input specifications and protocols, and the terminal sends this to the server in JSON or XML format. The input data contains detailed information necessary for the work, and the output is the appropriate formatting and transfer of this data to the server.

[0692] Step 2:

[0693] The server analyzes data received from terminals using a natural language processing engine. Specifically, it uses Python's NLTK and spaCy to extract important keywords and phrases from the data. The input is formatted business information, and the output is analyzed structured data. Based on this data, the subsequent business guideline generation process begins.

[0694] Step 3:

[0695] The server inputs the extracted information as prompts into a generation AI model, which then automatically generates business guidelines. Specifically, it uses OpenAI's GPT and outputs the generated business guidelines in text or PDF format. The input is structured data, and the output is a document containing business guidelines.

[0696] Step 4:

[0697] Users review the generated operational guidelines on their terminals. Specifically, they verify the content of the guidelines and input necessary corrections or improvement requests as feedback. The input is the operational guideline document, and the output is the feedback information.

[0698] Step 5:

[0699] The server receives user feedback and updates the operational guidelines. Machine learning algorithms are used to analyze the feedback and improve the guidelines. The input is feedback information, and the output is the updated operational guidelines. This update process refines the guidelines over time.

[0700] Step 6:

[0701] The server stores the updated business guidelines in a centralized management database. Specifically, this involves using SQL or NoSQL databases to establish a structure for storing, accessing, and sharing data. The input is the updated business guidelines, and the output is storage in the knowledge base and information sharing within the organization.

[0702] (Application Example 1)

[0703] 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".

[0704] In modern manufacturing, there is a demand for efficient management of operational information, optimization of work procedures, and improvement of product quality. In particular, developing automated systems capable of handling complex operational protocols is a challenge. However, conventional systems have limited efficient work operations and interdepartmental information sharing due to insufficient information acquisition, analysis, and feedback for improvement.

[0705] 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.

[0706] In this invention, the server includes means for acquiring business information, means for analyzing the acquired business information to automatically generate work procedures, means for updating the generated procedure manuals based on user feedback, means for storing and sharing the updated procedure manuals in a knowledge record, means for learning and improving the analysis model using user feedback, and means for the work machine to execute the work procedures based on product data and business protocols. This enables optimization of business processes and rapid information sharing.

[0707] "Business information" refers to all data related to the operation of a company or organization, including detailed information related to work procedures and product specifications.

[0708] A "work procedure" is a record that shows the specific steps and operations necessary to perform a particular task, and is compiled to improve work efficiency.

[0709] "Users" refer to individuals or teams who utilize this system for their work and contribute to improving the system's accuracy through feedback.

[0710] A "knowledge record" refers to a database or archive intended for sharing information within and outside an organization, preserving and making accessible accumulated knowledge.

[0711] "Feedback" refers to suggestions for improvement and opinions based on work performance provided by users, which are then used to improve the system's functionality.

[0712] "Working machinery" refers to hardware devices used to automate and perform specific manufacturing or processing tasks.

[0713] A "business protocol" refers to a set of instructions that define standardized procedures and rules necessary for carrying out business operations.

[0714] An "analysis model" refers to an algorithm or framework that processes business information using a computer program and creates products based on the analysis results.

[0715] The system for implementing this invention mainly consists of a server, a terminal, and a work machine. The server acquires business information and utilizes Python libraries such as NLTK and Spacy to analyze the data using natural language processing technology. The analyzed data is used to automatically generate work procedures using a generative AI model that utilizes TensorFlow and PyTorch.

[0716] The terminal is a device that receives input and feedback from users and sends the feedback information to the server. The server updates the work procedures based on the feedback, saves the results in a knowledge record, and organizes it in a format that can be shared within the company. This facilitates information sharing within the organization and contributes to the optimization of subsequent business processes.

[0717] The machines operate based on automatically generated work procedures received from the server, and are responsible for performing physical manufacturing tasks. This leads to increased efficiency in business processes and improved product quality.

[0718] As a concrete example, consider a scenario where a new bread manufacturing process is introduced in a factory. Users input new product specifications and operational protocols via a terminal and send them to a server. The server analyzes the information and sends automatically generated work procedures to the machines. The machines then begin the manufacturing process according to these procedures, and the procedures are improved in real time based on user feedback.

[0719] An example of a prompt for a generative AI model is: "Analyze the user's input data and generate a new dough processing procedure for bread making. The feedback was to shorten the mixing time, but please adjust it to maintain quality."

[0720] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0721] Step 1:

[0722] Users input business information using a terminal and send it to the server. This input information includes product specifications and business protocols. This input data is preprocessed on the server. For example, it is converted into a data format that facilitates analysis through normalization and tokenization of text data.

[0723] Step 2:

[0724] The server analyzes pre-processed business information and extracts necessary information using natural language processing techniques. This process includes named entity recognition and dependency analysis using NLTK and Spacy. The extracted information is input into a generative AI model. The generative AI model automatically generates work procedures based on the input data and sends the results to the next process.

[0725] Step 3:

[0726] The server receives the generated work procedure and sends it to the terminal. The user reviews the work procedure on the terminal and provides feedback as needed. Examples of feedback include suggestions for improving work efficiency and adjustments to the procedure. Feedback from the user is then entered.

[0727] Step 4:

[0728] Upon receiving feedback, the server updates the work procedure using the generated AI model. The updated procedure is an improved version that reflects user feedback. This improvement process involves continuous learning, improving the model's accuracy. The updated procedure is also saved in the knowledge log.

[0729] Step 5:

[0730] The server transmits updated work instructions to the machine. The machine performs the physical tasks according to the instructions. The performed tasks are required to be efficient in the product manufacturing process. Optimized procedures are used in this implementation.

[0731] 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.

[0732] This invention aims to more effectively manage business processes and generate and improve business procedures that take into account the emotional state of users. Specific embodiments of this system are described below.

[0733] System Configuration

[0734] This system primarily consists of a server, users, terminals, and an emotion engine. Users send work-related documents to the server via their terminals. The server uses a generative AI model to analyze the work data and automatically generate work procedure manuals. The emotion engine also analyzes the user's emotions and utilizes this information in the generation process.

[0735] Analysis of emotional data

[0736] The terminal captures the user's facial expressions and tone of voice through emotion recognition technology when the user views the work procedure manual. The server analyzes this emotion data to evaluate how the user feels about the manual. The evaluation results are used to optimize the content and expression of the manual.

[0737] Generating and updating procedure manuals

[0738] The server fine-tunes the instructions based on information from the emotion engine to ensure users can easily understand them. When user feedback is received, the server updates the instructions, and the emotion engine re-evaluates the user's stress level and satisfaction.

[0739] Reflection in the knowledge base

[0740] The updated procedure manuals are stored in the knowledge base. This facilitates information sharing between departments and project teams. Furthermore, the knowledge base contributes to the standardization and efficiency of work procedures.

[0741] Specific example

[0742] For example, when a customer support team reviews their procedures, the emotion engine detects when a user feels frustrated or confused while reading the manual. The server then rephrases the manual's explanation in a more understandable way and adds relevant multimedia content based on the detected emotion. As a result, support staff can perform their duties with reduced stress, and the quality of support improves.

[0743] In this way, by integrating and managing business data and user sentiment data, and realizing a dynamic and user-centered business procedure management system, it is possible to aim for improved operational efficiency and user experience across the entire organization.

[0744] The following describes the processing flow.

[0745] Step 1:

[0746] The user uploads work-related documents to the terminal. The terminal then sends these documents to the server and prepares to begin data processing.

[0747] Step 2:

[0748] The server applies an AI model to analyze the received business data. This model uses natural language processing techniques to analyze documents and identify business flows and procedures. The analysis results are stored in a template format.

[0749] Step 3:

[0750] The terminal collects user emotional data through an emotion engine while the user is viewing work procedure manuals. This process infers stress levels and satisfaction levels from the user's facial expressions, tone of voice, and other factors.

[0751] Step 4:

[0752] The server analyzes data from the emotion engine and reviews the content of the procedure manual. Based on the user's emotions, it adjusts the wording and tone of the procedure and adds supplementary information as needed.

[0753] Step 5:

[0754] Users utilize the generated instructions and provide feedback via their devices as needed. This feedback includes specific comments and suggestions for improvements.

[0755] Step 6:

[0756] The server updates the procedure manual based on data from the feedback and sentiment engine. These updates may include optimizing business processes and refining specific wording.

[0757] Step 7:

[0758] The server stores updated procedures in a knowledge base. This knowledge base is used for information sharing within the company and is accessible to other employees and departments.

[0759] Step 8:

[0760] The server uses an emotion engine to improve its analysis model. It works with feedback data to train the model to improve the accuracy of generating and updating business procedures.

[0761] (Example 2)

[0762] 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".

[0763] While documenting and standardizing work procedures contributes to improving organizational productivity, traditional methods struggle to consider users' emotional states, resulting in procedural documents that are difficult for users to understand. Furthermore, efficient evaluation and updating methods are needed when utilizing user feedback to improve procedures. Additionally, a system is required to share updated information across the entire organization.

[0764] 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.

[0765] In this invention, the server includes means for acquiring business information, means for sensing the user's facial expressions and tone of voice to collect emotional states, and means for extracting key points of business procedures and proposing optimizations using a generative AI model. This enables the generation of business procedure manuals that take user emotions into consideration, as well as the sharing and updating of information across the entire organization.

[0766] "Business information" refers to all data related to business operations performed within an organization, including procedures, records, input data, and user feedback.

[0767] A "procedure document" is a document that describes the steps and procedures necessary to carry out a task, and its purpose is to improve work efficiency and standardize operations.

[0768] "User facial expressions and tone of voice" refer to the nonverbal expressions and vocal characteristics that users exhibit when using business documents, and are used to estimate the user's emotional state.

[0769] "Emotional information" refers to data about a user's emotional responses and psychological state, obtained through analysis of facial expressions and tone of voice.

[0770] A "generative AI model" is a machine learning model that uses advanced artificial intelligence technologies to analyze data and automatically generate and update procedural documents.

[0771] "To propose an optimization" means to find the most efficient or effective method or means under specific conditions and present it in a feasible form.

[0772] A "knowledge base" is a database system that centrally manages and stores business procedures and related information, enabling information sharing within an organization.

[0773] The specific configuration and operation of this system are shown to illustrate embodiments for carrying out the invention.

[0774] This system aims to generate and optimize business procedure manuals and consists mainly of a server, terminals, and users. The server is equipped with a high-performance processor and sufficient memory, and a generative AI model runs on it to analyze business data. The generative AI model receives business information provided by users and automatically generates procedure documents based on that data. Furthermore, the model optimizes the document content by incorporating emotional information into the model.

[0775] The device has software installed that detects the user's facial expressions and tone of voice, and is equipped with the ability to capture this data through the camera and microphone. The device sends this data to a server, which analyzes it and evaluates the user's emotional state. This makes it possible to generate procedural documents that take the user's emotions into consideration.

[0776] For example, if a user is using a new product's shipping procedure manual and encounters an unclear step, causing them to show confusion, the terminal will detect this expression. The server will then use this emotional information to create a prompt instructing the generative AI model to "update the steps to be concise and clear," thereby revising the procedure manual. In this way, user feedback is reflected in the procedure manual in real time.

[0777] As a concrete example, a prompt could be issued to the generating AI model with the instruction, "Update the product shipping procedure manual to be concise and clear, and make improvements based on sentiment data." Based on this instruction, the server analyzes business information and improves the procedure document.

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] Users prepare work-related documents via their terminals and send them to the server. The input consists of work-related information, which may include existing procedures and feedback. Specifically, the user selects a document and presses the send button.

[0781] Step 2:

[0782] The server performs data analysis by inputting received business information into a generating AI model. The model analyzes the business information, extracts the key points of the procedure manual, and generates an initial procedure document. The output is the generated procedure document. Specifically, the model summarizes the text and organizes the procedures.

[0783] Step 3:

[0784] The device uses its camera and microphone to capture facial expressions and voice tone as the user views generated procedure documents. Input is non-verbal data from the user, and output is sent to the server as emotional information. The device captures information in real time.

[0785] Step 4:

[0786] The server analyzes emotional information transmitted from the terminal and evaluates the user's emotional state. The analysis reveals the user's psychological response to the procedural document. The input is emotional information, and the output is the user's emotional evaluation. Specifically, the server uses an emotional recognition algorithm.

[0787] Step 5:

[0788] The server sends prompts to the generative AI model based on sentiment evaluation, and adjusts the procedure document. For example, if the user shows a confused reaction, it sends a prompt to "simplify the steps." The input is the prompt, and the output is the updated procedure document. Specifically, the server highlights key points and adds explanations.

[0789] Step 6:

[0790] The updated procedure document is stored in the knowledge base and shared within the organization. The input is the updated procedure document, and the output is the addition of an entry to the knowledge base. The server sends the data to the knowledge management system, and the information is stored.

[0791] This series of steps ensures that operational procedures are always kept up-to-date and user-friendly, and enables efficient information sharing throughout the organization.

[0792] (Application Example 2)

[0793] 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".

[0794] Traditional business procedure systems have struggled to provide dynamic procedures that take into account user emotions and states, resulting in limited improvements to the user experience. This can lead to decreased operational efficiency and lower customer satisfaction, highlighting the need for adaptive and user-centered procedure management systems.

[0795] 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.

[0796] In this invention, the server includes means for acquiring business data, means for analyzing the acquired business data to automatically generate business procedures, means for updating the generated procedure manuals based on user feedback, means for analyzing an individual's emotional state using emotion recognition technology, and means for adaptively changing the business procedures based on the analyzed emotional state. This makes it possible to reflect the user's emotional state in real time and provide optimized procedure manuals.

[0797] "Business data" refers to data that includes information related to work and transactions, and serves as basic information for systems to process and analyze.

[0798] "Business procedures" are a set of steps and instructions that outline how to perform a specific task, serving as a guideline for efficient and effective work execution.

[0799] A "procedure manual" is a document that describes in detail the steps and methods for performing a task, serving as a guideline to help employees understand their work.

[0800] A "knowledge base" is a database that stores information and procedures related to business operations, and is used for sharing and reusing them throughout the organization.

[0801] "Emotion recognition technology" is a technology that analyzes an individual's facial expressions, voice, and behavior to determine their emotions, and is a means for computers to understand human emotions.

[0802] "Emotional state" refers to the state in which an individual exhibits psychological and physiological responses in a particular situation, and is a factor that significantly influences the user experience.

[0803] "Adaptive modification" refers to a method where the system automatically adjusts and updates itself in response to changes in circumstances and users, thereby achieving optimal results.

[0804] The system implementing this invention mainly consists of a server, an emotion recognition terminal, and a user. The server receives business data and automatically generates business procedures using a generative AI model. The emotion recognition terminal analyzes the user's facial expressions and tone of voice as they use the procedure manual and evaluates their emotions in real time. The server takes in the user's emotional state and feedback and dynamically adjusts the business procedures based on this information.

[0805] Specifically, the emotion recognition terminal uses smart glasses, allowing users to perform tasks while visually obtaining information. The software implements EmotionAPI and real-time voice tone analysis as emotion recognition technologies, thereby precisely determining the user's emotions. The generative AI model uses the latest natural language processing technology to provide work procedures that respond to user feedback and emotional states.

[0806] For example, when introducing a new product in a physical store, if emotion recognition technology detects that the user is confused, the server can use a generative AI model to generate a "prompt message to provide a clearer explanation" and display it through the emotion recognition terminal. This allows the sales staff to quickly provide appropriate explanations to capture the customer's attention, thereby improving the customer experience.

[0807] An example of a prompt for the generating AI model would be, "Provide a simplified explanation for a customer who seems confused about the new product features," which helps generate appropriate guidance based on the user's situation.

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The device captures the user's facial expressions and voice tone in real time. It takes raw data from the camera and microphone as input. This data is analyzed using emotion recognition technology to output the user's emotional state (e.g., satisfaction, confusion, anxiety). Specifically, it uses the EmotionAPI to identify emotions from facial muscle movements and voice intonation.

[0811] Step 2:

[0812] The server automatically generates appropriate work procedure manuals using a generative AI model based on acquired emotional state data. The input consists of the user's emotional state and related work data. The generative AI model analyzes this data to generate optimal explanations and customer service procedures. The output is a procedure manual that is easy for the user to understand. The system generates prompt statements based on the emotional data, and the content corresponding to these prompts is reflected in the procedure manual.

[0813] Step 3:

[0814] The terminal displays the generated procedure manual, allowing the user to visually confirm it. The input is the content of the procedure manual sent from the server. As output, the procedure manual is displayed on a screen such as smart glasses in a user-friendly format. Specifically, the procedure manual, including supplementary explanations tailored to the user's emotions, is presented on the screen.

[0815] Step 4:

[0816] Users perform tasks based on the displayed instructions and input feedback into the terminal as needed. This feedback is sent to the server and used to improve future instructions. Input methods include feedback forms and voice input, and output is points for improvement in the instructions. Operationally, user feedback is recorded in a digital form and stored on the server.

[0817] Step 5:

[0818] The server analyzes user feedback and compares it with emotional states to improve future procedures. Inputs are past emotional data and feedback data. Output is an updated analysis model. Specifically, a generative AI model learns from the feedback and works to generate more effective prompts.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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."

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] Means of acquiring business data,

[0843] A means of automatically generating business procedures by analyzing acquired business data,

[0844] A means of updating the generated procedure manual based on user feedback,

[0845] A means of saving and sharing updated procedure manuals in a knowledge base,

[0846] A means of learning and improving the analysis model using user feedback,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which proposes the optimization of business procedures based on the results of business data analysis.

[0850] (Claim 3)

[0851] The system according to claim 1, which includes multimedia content in the generated procedure manual and displays it visually.

[0852] "Example 1"

[0853] (Claim 1)

[0854] Means of obtaining information related to work,

[0855] A means for automatically generating business guidelines using natural language processing means for analyzing acquired business information,

[0856] A means of updating the generated operational guidelines based on user feedback,

[0857] A means of storing and disseminating updated operational guidelines in a centralized management database,

[0858] A means of learning and improving analysis algorithms using user feedback,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, which proposes the optimization of business guidelines based on the results of the analysis of business information.

[0862] (Claim 3)

[0863] The system according to claim 1, which includes composite media information in the generated business guidelines and displays it visually.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] Means of obtaining business information,

[0867] A means of automatically generating work procedures by analyzing acquired business information,

[0868] A means of updating the generated procedure manual based on user feedback,

[0869] A means of saving and sharing updated procedure manuals in a knowledge record,

[0870] A means of learning and improving the analysis model using user feedback,

[0871] A means by which a work machine executes work procedures based on product data and work protocols,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which proposes the optimization of work procedures based on the results of analyzing business information.

[0875] (Claim 3)

[0876] The system according to claim 1, which includes multimedia information in the generated procedure manual and displays it visually.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] Means of obtaining business information,

[0880] A means of automatically generating procedural documents by analyzing acquired business information,

[0881] A means of detecting the user's facial expressions and tone of voice to collect emotional state,

[0882] A means of analyzing collected emotional information and evaluating the user's emotional state,

[0883] Means for adjusting and updating the generated procedure document based on user sentiment evaluation and feedback,

[0884] A means of storing updated procedural documents in a knowledge base and sharing information,

[0885] A means of learning and improving an analytical model using user sentiment evaluations and feedback,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which uses a generative AI model to extract key points of business procedures and proposes optimization.

[0889] (Claim 3)

[0890] The system according to claim 1, which adds visual and auditory content to a generated procedural document and presents it visually.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] Means of acquiring business data,

[0894] A means of automatically generating business procedures by analyzing acquired business data,

[0895] A means of updating the generated procedure manual based on user feedback,

[0896] A means of saving and sharing updated procedure manuals in a knowledge base,

[0897] A means of learning and improving the analysis model using user feedback,

[0898] A means of analyzing an individual's emotional state using emotion recognition technology,

[0899] A means of adaptively changing work procedures based on analyzed emotional states,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, which proposes the optimization of work procedures based on the results of business data analysis and the emotional state of individuals.

[0903] (Claim 3)

[0904] The system according to claim 1, which includes multimedia representations in the generated procedure manual and presents them visually. [Explanation of Symbols]

[0905] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of acquiring business data, A means of automatically generating business procedures by analyzing acquired business data, A means of updating the generated procedure manual based on user feedback, A means of saving and sharing updated procedure manuals in a knowledge base, A means of learning and improving the analysis model using user feedback, A system that includes this.

2. The system according to claim 1, which proposes the optimization of business procedures based on the results of business data analysis.

3. The system according to claim 1, which includes multimedia content in the generated procedure manual and displays it visually.

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