System

Abstract

A system is provided.SOLUTION: A system, comprising: means for uploading business knowledge documents; means for having a generative AI model that analyzes the uploaded documents; means for detecting inconsistencies and ambiguous expressions by the generative AI model and generating concrete improvement proposals; means for automatically categorizing and storing the analyzed documents in a database; means for providing relevant information from the stored documents based on a user's query; and means for responding to a user's question in a chat fashion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In many companies, business knowledge and know-how are kept private and not shared. This hinders business efficiency and rapid problem solving. Furthermore, when shared information contains inconsistencies or unclear expressions, the reliability of the information decreases and it is often not used effectively. Furthermore, necessary information is often not found through keyword searches for each tool, creating barriers to information access. The objective of this invention is to solve these problems and provide an environment in which business knowledge can be used efficiently. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing the following means. A system is provided that includes a means for uploading business knowledge documents, a means having a generative AI model that analyzes the uploaded documents, a means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals, a means for automatically categorizing the analyzed documents and storing them in a database, a means for providing related information from the stored documents based on a user's search query, and a means for instantly responding to user questions in a chat format. This enables efficient storage and sharing of business knowledge and creates an environment in which users can quickly obtain the information they need.

[0006] A "business knowledge document" is a document that records the knowledge, know-how, information, etc. that a company or individual has acquired in the course of performing their business.

[0007] A "generative AI model" is a system that uses artificial intelligence technologies such as machine learning and deep learning to analyze text and data and make improvement suggestions.

[0008] An "inconsistency" is a state in which the data or information within a document is inconsistent or contradictory.

[0009] "Obscure expression" refers to text in a document that is unclear and difficult to understand.

[0010] "Improvement proposals" are specific proposals to correct inconsistencies or unclear expressions and to make the content clearer and more accurate.

[0011] "Categorization" is the process of classifying the contents of analyzed documents into specific categories.

[0012] A "tag" is a keyword or label given to a document to indicate its content or nature.

[0013] A "database" is a collection of data organized in a particular way and a system that allows that data to be efficiently accessed, managed, and searched.

[0014] A "search query" is a keyword or phrase that a user enters to search for specific information.

[0015] "Chat-style" is an interactive interface in which the user inputs a question in natural language and the system responds immediately.

[0016] "User" refers to a person who operates the system and uses its functions.

[0017] A "terminal" is an electronic device such as a computer, smartphone, or tablet that a user uses to access the system. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] MODE FOR CARRYING OUT THE INVENTION

[0040] This invention is a system for automatically storing and effectively utilizing business knowledge. This system uses a generative AI model to review, improve, and appropriately categorize business knowledge documents, allowing users to smoothly obtain the information they need.

[0041] Document upload and parsing

[0042] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0043] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0044] Knowledge peer review and improvement

[0045] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0046] Information structuring and automatic categorization

[0047] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0048] Knowledge sharing and discovery

[0049] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0050] Chat-style support

[0051] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0052] The above is an embodiment of the present invention. This system makes it possible to efficiently store and share business knowledge and provide an environment in which users can quickly and accurately obtain the information they need.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] A user uploads a business knowledge document to the system using a terminal. The user opens a browser and accesses the system's upload page. Then, the user clicks the "Select File" button to select the document file and clicks the "Upload" button.

[0056] Step 2:

[0057] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0058] Step 3:

[0059] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0060] Step 4:

[0061] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0062] Step 5:

[0063] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0064] Step 6:

[0065] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0066] Step 7:

[0067] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0068] Step 8:

[0069] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0070] Step 9:

[0071] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0072] Step 10:

[0073] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[0074] Step 11:

[0075] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[0076] Step 12:

[0077] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[0078] Through these steps, the system enables efficient storage and sharing of business knowledge, providing an environment in which users can quickly and accurately obtain the information they need.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] The vast volume and complexity of business knowledge data makes it difficult to efficiently organize it and quickly obtain the necessary information. Furthermore, the manual correction of data containing inconsistencies or unclear expressions takes time and effort, so there is a need to improve efficiency.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes: means for a user to upload business knowledge data using an information processing device; means for receiving the uploaded data and passing it to a generative AI model as an analysis target; means for analyzing the data using the generative AI model to detect inconsistencies and unclear expressions and generate specific improvement proposals; means for automatically categorizing the analyzed data, adding associated tags, and saving the data as structured data in a database; means for quickly providing related information from the saved data based on a user's search query; and means for generating answers to user questions in real time using the generative AI model and providing immediate responses in chat format. This allows business knowledge data to be organized efficiently, making it possible to quickly and accurately obtain necessary information.

[0084] A "user" is an individual or organization that uses the system to upload and search business knowledge data.

[0085] An "information processing device" is a device that a user uses to upload business knowledge data, and includes a personal computer, a tablet, a smartphone, and the like.

[0086] "Business knowledge data" refers to documents, files, or data sets that contain business-related knowledge or information.

[0087] The "upload interface" is an online interface that allows users to send business knowledge data to the server.

[0088] A "generative AI model" is an artificial intelligence model that analyzes received data, detects inconsistencies and unclear expressions, and generates specific improvement proposals.

[0089] The "server" is a central computer system that receives, stores, and analyzes uploaded business knowledge data, drives generative AI models, and executes various processes.

[0090] "Analysis target" refers to the data that will be analyzed by the generative AI model after being uploaded to the server.

[0091] An "inconsistency" is an area in the data where there is a lack of consistency or contradiction.

[0092] "Unclear expression" means an expression in which the data content is vague and difficult to understand.

[0093] "Specific improvement suggestions" are correction suggestions automatically generated by the generative AI model to resolve inconsistencies or unclear expressions.

[0094] "Categorization" is the process of classifying analyzed data into specific categories.

[0095] "Tags" are additional information that add highly relevant keywords to data to make it easier to search for later.

[0096] A "database" is a digital storage device for storing structured data that has been analyzed and archived.

[0097] A "search query" is a keyword or phrase that a user enters to search for desired information.

[0098] "Chat format" means that users input questions in real time and the system responds immediately.

[0099] "Generating answers in real time" means automatically and quickly generating answers immediately after a user enters a question.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] This invention is a system for efficiently managing business knowledge data and enabling users to quickly and accurately obtain the information they need. This system uses a generative AI model to analyze business knowledge data and automatically detect and correct data inconsistencies and unclear expressions. It also automatically categorizes the analyzed data, adds tags, and stores it in a database as structured data, providing an environment that makes it easy for users to search.

[0102] Uploading and saving documents

[0103] A user uploads business knowledge data using an information processing device (e.g., a PC or tablet). The user selects a file through the system's upload interface and sends it to the system. For example, if a user uploads an Excel file (e.g., excel_file.xlsx), the file is sent to the server and stored in the database along with metadata (upload date and time, file size, etc.).

[0104] Document Parsing

[0105] The server passes the stored document to a generative AI model for analysis. The generative AI model scans the document for inconsistencies and unclear expressions. For example, it reads the contents of each cell in an Excel spreadsheet and checks for inconsistencies.

[0106] Knowledge peer review and improvement

[0107] The server receives the analysis results of the generative AI model and automatically generates specific improvement proposals for detected inconsistencies and unclear expressions. For example, to a cell that simply says "Sales forecast," add a detailed explanation such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry.

[0108] Information structuring and automatic categorization

[0109] The server automatically categorizes the analyzed documents using the generative AI model. For example, a document related to "project management" would be classified under the "project management" category. Additionally, related tags such as "budget planning" and "marketing strategy" are automatically added as needed and saved in the database.

[0110] Knowledge sharing and discovery

[0111] A user accesses the system's search interface using a terminal and enters a query, for example, "marketing strategy for a new product," into the search box. The server then quickly retrieves relevant documents from the database and displays them to the user. The retrieved documents are presented in a list format in order of relevance.

[0112] Chat-style support

[0113] A user can use the system's chat interface to input a question. For example, "How do I create a budget plan for next year?" The server analyzes this question using a generative AI model and automatically generates an appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to review its contents and obtain the necessary information.

[0114] Specific operation example

[0115] Upload: The user uploads an Excel file (excel_file.xlsx) from their computer. The server receives and saves this file.

[0116] Analysis: The server passes the file to the generative AI model for analysis. For example, if cell B4 contains only "Sales Forecast," the generative AI model will detect the inconsistency.

[0117] Improvement: The server adds a specific improvement proposal: "Sales forecast (Q1 FY2023) - Estimated amount: 5 million yen."

[0118] Categorize: The server categorizes the file into the "Project Management" category and adds the "Budget Planning" tag.

[0119] Search: When a user searches for "marketing strategy for a new product," relevant documents are displayed.

[0120] Chat support: When a user types, "How do I create a budget plan for next year?", the server instantly generates and displays the appropriate answer.

[0121] Prompt Sentence Examples

[0122] Below are some examples of prompt sentences.

[0123] Document Upload: "Upload your business knowledge data. The system will analyze the data and detect inconsistencies or unclear wording."

[0124] Improved analysis results: "The generative AI model detected the following unclear areas and provided specific suggestions for improvement."

[0125] Enter a search query: "Enter 'marketing strategy for a new product' in the system's search interface. Relevant documents will be displayed."

[0126] Chat support example: "Type 'How do I create a budget plan for next year?' into the chat interface."

[0127] The above is an embodiment of the present invention. By using this system, business knowledge data can be efficiently organized, and necessary information can be obtained quickly and accurately.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] A user uploads business knowledge data (e.g., excel_file.xlsx) using an information processing device. The user clicks the "Select File" button in the system's upload interface, selects a file, and presses the upload button. The input here is the business knowledge data, and the output is the data sent to the server.

[0131] Step 2:

[0132] The server receives the uploaded data and stores it in a database along with metadata (upload date and time, file size, etc.). At this time, the server prepares the received data to be passed to the generative AI model for analysis. The input is the uploaded business knowledge data, and the output is the stored data.

[0133] Step 3:

[0134] The server passes the stored data to the generative AI model, which then begins analysis. The generative AI model scans the data to detect inconsistencies and unclear expressions. In this case, the input is business knowledge data, and the output is the analysis results. For example, each cell in an Excel spreadsheet is read one by one, and the contents are checked for inconsistencies.

[0135] Step 4:

[0136] The server automatically generates specific improvement proposals based on the analysis results obtained from the generative AI model. For example, for a cell that simply says "Sales forecast," it generates a detailed improvement proposal such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry. The input here is the analysis result from the generative AI model, and the output is a new document that reflects the improvement proposal.

[0137] Step 5:

[0138] The server automatically categorizes the data analyzed and improved by the generative AI model. For example, data related to "project management" is classified into the "project management" category and related tags such as "budget planning" and "marketing strategy" are automatically added. The input is a document with the proposed improvements reflected, and the output is categorized and tagged structured data.

[0139] Step 6:

[0140] The server stores the categorized data in a database. The data is stored as structured data with category and tag information, making it easier to search later. The input is data with categories and tags, and the output is the data stored in the database.

[0141] Step 7:

[0142] A user accesses the system's search interface using a terminal and enters a search query, for example, "marketing strategy for a new product" in the search box. The server retrieves relevant information from the database based on the entered query and displays it to the user. Here, the input is the search query, and the output is a list of relevant documents.

[0143] Step 8:

[0144] A user enters a question into the system's chat interface. For example, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate an appropriate answer. The generated answer is immediately displayed in the chat window. The input here is the user's question, and the output is the answer from the generative AI model.

[0145] The above are the specific processing steps of this system. The specific operations performed at each step are necessary to achieve efficient management of business knowledge data and rapid information acquisition.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional business knowledge management systems have problems with inefficient document uploading, analysis, improvement, categorization, search, and real-time response. Especially in large-scale business environments such as logistics centers, manual business knowledge management is time-consuming, labor-intensive, and inefficient. Furthermore, there is no established method for utilizing general-purpose computing devices such as smartphones, making it difficult to respond immediately on-site.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes: means for uploading business knowledge documents; means for having a generative AI model that analyzes the uploaded documents; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed documents and saving them in a database; means for providing related information from the saved documents based on a user's search query; means for instantly responding to user questions in chat format; means for scanning and uploading business knowledge documents via a smartphone or general-purpose computing device; and means for classifying the business knowledge documents analyzed by the generative AI model into categories such as project management, quality control, and inventory management. This enables efficient management of business knowledge, rapid on-site response, and appropriate categorization.

[0151] A "business knowledge document" is a document that contains knowledge and information about business in a company or organization.

[0152] "Document upload" is an operation in which a user sends their own electronic files or scanned paper documents to the system.

[0153] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate improvement suggestions based on the analysis results.

[0154] "Inconsistencies and unclear expressions" refers to conflicting information or parts of a document that are difficult to understand.

[0155] "Specific improvement proposals" are proposals based on the analysis results to make the content of the document clearer and more accurate.

[0156] "Automatic categorization" is the process of classifying analyzed documents into appropriate categories using a generative AI model.

[0157] A "database" is a data storage system that structures and stores parsed documents and other related information.

[0158] A "search query" is a keyword or phrase that a user enters into a system to retrieve desired information.

[0159] "Chat-style instant response" is an interface that provides answers to questions from users in real time.

[0160] A "smartphone or general-purpose computing device" is a portable electronic device or general-purpose computer used to perform operations such as scanning, uploading, and searching.

[0161] "Generative AI categorization" is the process by which a generative AI model analyzes the content of a document and appropriately classifies it into categories such as project management, quality control, or inventory management.

[0162] The system described in this invention, "LogiMaster," efficiently manages business knowledge documents and is intended for use in logistics centers in particular. This system is realized using the following hardware and software.

[0163] Hardware used

[0164] Server: A central computing device that stores, analyzes, and processes documents.

[0165] Smartphone or general-purpose computing device: A handheld device or general-purpose computer for scanning documents, uploading documents, entering search queries, and chatting.

[0166] Software used

[0167] Flask: Used as a web framework to provide the user interface and API endpoints.

[0168] OpenAI API: Responsible for analyzing documents with generative AI models and generating improvement suggestions.

[0169] SQLAlchemy: An ORM library for database management that uses SQLite as a backend.

[0170] Data processing and calculation

[0171] The server receives business knowledge documents uploaded by users and analyzes and proposes improvements through a generative AI model. Documents uploaded using smartphones or general-purpose computing devices are processed as follows:

[0172] Document upload: Users can use their smartphone camera to scan paper documents or directly upload existing electronic files.

[0173] Analysis and improvement suggestions: Documents sent to the server are analyzed by a generative AI model using the OpenAI API to detect inconsistencies and unclear expressions and suggest specific improvement suggestions.

[0174] Categorization and database storage: Parsed documents are automatically categorized into categories such as project management, quality control, inventory management, etc. and stored in a database using SQLAlchemy.

[0175] Search and chat functionality: When a user enters a search query, the server extracts and provides relevant information from stored documents, and a generative AI model responds immediately to the user's questions using a real-time chat interface.

[0176] Specific examples

[0177] For example, if a logistics center manager wants to search for "new ways to optimize inventory procedures," he or she can enter the following query on a smartphone:

[0178] How can you optimize your new receiving procedures?

[0179] Based on this query, the server uses a generative AI model to search for relevant business knowledge documents and provide the most appropriate information.

[0180] A specific example of a prompt sentence for a generative AI model is as follows:

[0181] Please analyze the following business knowledge document and suggest improvements:

[0182] ---

[0183] Document Title: "Logistics Center Financial Report"

[0184] 1. Consider ways to reduce inventory costs

[0185] 2. Improving work efficiency

[0186] 3. Automation of work through mechanization

[0187] 4. ...

[0188] Please analyze and suggest improvements.

[0189] In this way, the system based on the present invention efficiently manages business knowledge and provides support for quickly obtaining required information.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] A user uploads a business knowledge document using a smartphone or general-purpose computing device. Specifically, the user scans a paper document with the smartphone camera or selects an existing electronic file and presses the upload button. The input data is the document file, and the output is the data sent to the server.

[0193] Step 2:

[0194] The server saves the uploaded document. Specifically, it receives the document file using Flask and saves it in a specific folder on the server. The input data is the data sent by the user, and the output is a file saved on the server.

[0195] Step 3:

[0196] The server passes the saved document to the generative AI model for analysis. Specifically, it uses the OpenAI API to send the document content to the analysis model in the form of a prompt. The input data is the saved document content, and the output is the analysis result of the generative AI model.

[0197] Step 4:

[0198] The generative AI model detects inconsistencies and unclear expressions in the document and proposes specific improvement suggestions. Specifically, the generative AI model extracts improvement suggestions from the analysis results it provides and adds them to the document as new comments or corrections. The input data are the analysis results, and the output is a new document containing the improvement suggestions.

[0199] Step 5:

[0200] The server automatically categorizes documents analyzed by the generative AI model, specifically assigning categories such as project management, quality control, and inventory management based on the document content. The input data is the refined document content, and the output is the classified category.

[0201] Step 6:

[0202] The server stores the categorized documents in a database. Specifically, it uses SQLAlchemy to store the document content and category information in the database. The input data is the document with classification information, and the output is the saved state of the database.

[0203] Step 7:

[0204] A user inputs a search query using a smartphone or a general-purpose computing device. Specifically, the user inputs keywords into a search interface and presses a search button. The input data is the search query, and the output is a request for search results.

[0205] Step 8:

[0206] The server searches for and provides relevant documents in the database based on the search query. Specifically, it uses SQLAlchemy to extract documents that match the query and display them to the user. The input data is the user's search query, and the output is a list of relevant documents.

[0207] Step 9:

[0208] A user inputs a question in real time using the chat interface. Specifically, the user inputs a question in the chat window and presses the send button. The input data is a chat-style question, and the output is a request for an immediate response.

[0209] Step 10:

[0210] The server uses a generative AI model to respond to the user's questions immediately. Specifically, the input question is passed to the AI ​​model as a prompt, and the generated answer is displayed to the user. The input data is the question, and the output is the answer from the generative AI model.

[0211] Through these steps, efficient management of business knowledge and rapid response in the field will be achieved.

[0212] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0213] MODE FOR CARRYING OUT THE INVENTION

[0214] This invention is a system for automatically storing and effectively utilizing business knowledge. This system combines a generative AI model and an emotion engine to review, refine, categorize, and search business knowledge documents, and provides information based on user emotions.

[0215] Document upload and parsing

[0216] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0217] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0218] Knowledge peer review and improvement

[0219] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0220] Information structuring and automatic categorization

[0221] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0222] Knowledge sharing and discovery

[0223] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0224] Chat-style support

[0225] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0226] Use of emotion engine

[0227] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[0228] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[0229] The server can also store the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements, allowing the system to provide more appropriate information according to the user's emotional state.

[0230] In this way, the present invention utilizes generative AI models and emotion engines to enable efficient storage, sharing, and search of business knowledge, as well as optimization of user experience.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] A user uploads a business knowledge document to the system using a terminal. For example, the user opens a browser, accesses the system's upload page, clicks the "Select File" button, selects the document file, and clicks the "Upload" button.

[0234] Step 2:

[0235] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0236] Step 3:

[0237] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0238] Step 4:

[0239] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0240] Step 5:

[0241] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0242] Step 6:

[0243] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0244] Step 7:

[0245] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0246] Step 8:

[0247] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0248] Step 9:

[0249] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0250] Step 10:

[0251] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[0252] Step 11:

[0253] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[0254] Step 12:

[0255] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[0256] Step 13:

[0257] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface, the tone of their search queries, etc.

[0258] Step 14:

[0259] The emotion engine analyzes the user's emotional state and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[0260] Step 15:

[0261] The server accumulates the emotional data obtained from the emotion engine as feedback and reflects it in future system improvements, enabling the system to provide more appropriate information according to the user's emotional state.

[0262] Through these steps, the system enables efficient storage, sharing, and search of business knowledge, as well as the provision of information based on user emotions.

[0263] Example 2

[0264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] In conventional business knowledge management systems, storing and searching knowledge documents is done manually, requiring a great deal of time and effort. Furthermore, detecting inconsistencies and unclear expressions and proposing improvements is manual, making efficient improvements difficult. Furthermore, information is not provided based on the user's emotional state, resulting in low user convenience and satisfaction. There was a need to provide an efficient and flexible business knowledge management system that could solve these issues.

[0266] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0267] In this invention, the server includes: means for uploading business knowledge data; means having a generative AI model for analyzing the uploaded data; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed data and saving it in a database; means for providing related information from the saved data based on a user's search query; means for instantly responding to user questions in chat format; means for passing real-time input data from the user to an emotion engine and analyzing the emotional state; and means for adapting system operation based on the analysis results of the emotion engine and accumulating feedback. This enables efficient storage, analysis, improvement, and sharing of business knowledge data, and optimal information provision based on the user's emotional state.

[0268] "Business knowledge data" is electronic data that records knowledge and information related to business.

[0269] "Uploading means" is a function that allows a user to send data from their own terminal to the server.

[0270] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and suggest improvements.

[0271] "Means for detecting inconsistencies and ambiguities" refers to the ability to use generative AI models to find errors or ambiguities in the data.

[0272] The "means for generating specific improvement proposals" is a function that automatically suggests appropriate correction methods for detected inconsistencies or unclear expressions.

[0273] "Means for automatically categorizing analyzed data and storing it in a database" refers to a function that classifies analyzed data based on its meaning, tags it appropriately, and stores it in a database.

[0274] "Means for providing relevant information from stored data based on a user's search query" refers to a function that extracts and provides appropriate information from a database based on search keywords entered by the user.

[0275] The "means for immediate response in chat format" is a function that responds in real time to questions entered by the user through a chat interface.

[0276] An "emotion engine" is an algorithm that analyzes a user's text input and behavioral data to understand the user's emotional state.

[0277] The "means for passing user input data to the emotion engine and analyzing the emotional state" is a function for sending user input information to the emotion engine and analyzing the user's emotions based on the results.

[0278] "Means for adapting the system's behavior based on the analysis results of the emotion engine" is a function that adjusts the system's information provision method and response content based on the user's emotional state.

[0279] "Means for accumulating feedback" is a function that records the analysis results of the emotion engine and user reactions in the system and uses them for future improvements.

[0280] This invention is a system that combines a generative AI model and an emotion engine to efficiently manage, analyze, improve, share, and search business knowledge data, and provide information based on user emotions as appropriate. A specific embodiment of this system will be described below.

[0281] System configuration

[0282] The system includes the following major hardware and software components:

[0283] 1. Server: The central hardware for data processing and storage.

[0284] 2. Terminal: A device (such as a personal computer or smartphone) that provides an interface for users to access the system.

[0285] 3. Generative AI model: A software algorithm that analyzes business knowledge data and generates specific improvement proposals.

[0286] 4. Emotion engine: An algorithm that analyzes user input data and adjusts the system's behavior based on the user's emotional state.

[0287] Program processing

[0288] Document upload and parsing

[0289] Users use their terminals to upload their own business knowledge data to the system. For example, a user selects a business report (Excel file) on their computer and uploads the file through the system's upload interface. At this stage, the data is sent to the server and saved.

[0290] The server passes the received data to a generative AI model for analysis. The generative AI model scans the document content to detect inconsistencies and unclear expressions. The specific software used is an analysis tool that uses natural language processing (NLP).

[0291] Knowledge peer review and improvement

[0292] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the data. For example, it detects incorrect data input or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the data as new entries.

[0293] Information structuring and automatic categorization

[0294] The server automatically categorizes the data analyzed by the generative AI model. For example, if the data content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores the data in a database as structured data for future searches.

[0295] Knowledge sharing and discovery

[0296] Users use their devices to access the system's search interface and search for the information they need. For example, they enter a search query like "marketing strategy for a new product." The server quickly extracts relevant data from its stock database and presents it to the user, allowing them to smoothly access the information they need.

[0297] Chat-style support

[0298] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0299] Use of emotion engine

[0300] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[0301] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more friendly and detailed explanation. The server can also accumulate the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements.

[0302] Examples and prompts

[0303] For example:

[0304] 1. Document upload: "A user uploads an Excel file of a sales report from their computer to the system."

[0305] 2. Information search: "The user types 'tell me about the latest technology trends' into the search box, and the server provides the latest relevant technology reports."

[0306] 3. Chat support: "Users can type 'how to set up cloud storage' into the chat window, and the system will prompt them with the setup instructions."

[0307] Example prompts to input to a generative AI model:

[0308] Please analyze the sales report.

[0309] Tell us about the latest technology trends.

[0310] How do I set up cloud storage?

[0311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0312] The flow of this system's program processing

[0313] Step 1:

[0314] Users use their terminals to upload business knowledge data to the system. For example, users select a business report (Excel file) on their terminal and upload the file through the system's upload interface. The input is the user's business knowledge file, and the output is that the file is sent to the server and saved.

[0315] Step 2:

[0316] The server passes the uploaded data to a generative AI model, which analyzes the content of the data and detects inconsistencies and unclear expressions. For example, the server passes the data through an NLP (natural language processing) tool to perform word frequency and context analysis. The input is business knowledge data, and the output is a list of inconsistencies and unclear expressions as a result of the analysis.

[0317] Step 3:

[0318] The server automatically generates specific improvement proposals based on the analysis results received from the generative AI model. For example, the generative AI model has the function of presenting "inconsistent data" and "correct data examples." The input is the analysis results of the generative AI model, and the output is new knowledge data with improvement proposals added.

[0319] Step 4:

[0320] The server automatically categorizes the analyzed and improved data and stores it in a database. For example, it can be classified into categories such as "project management" or "marketing." The input is knowledge data with improvement suggestions added, and the output is categorized and stored in the database as structured data.

[0321] Step 5:

[0322] A user uses a terminal to access the system's search interface and search for the information they need. For example, they enter a keyword such as "marketing strategy for a new product." The input is the user's search query, and the output is the relevant information in the database.

[0323] Step 6:

[0324] The server extracts relevant information from the database based on the search query and provides it to the user. For example, the server filters the appropriate data and displays the top results. The input is the user's search query, and the output is the filtered relevant information.

[0325] Step 7:

[0326] The user uses the chat interface of the system to input a question in real time, for example, "How do I create a budget plan for next year?" The input is the user's real-time question, and the output is the question being sent to the server.

[0327] Step 8:

[0328] The server uses a generative AI model to analyze the user's question and generate the optimal answer. For example, the generative AI model refers to past data to suggest an appropriate answer. The input is the user's question data, and the output is the generated answer.

[0329] Step 9:

[0330] The server instantly displays the generated response in the chat window for the user. At this time, the emotion engine analyzes the user's text input and determines their emotional state. The input is the response from the generative AI model and the user's text input, and the output is an optimized response and feedback of the emotional state.

[0331] Step 10:

[0332] Based on the emotion analysis results from the emotion engine, the server adjusts the system's behavior. For example, if the user is feeling frustrated, the system will provide a detailed and helpful explanation. The input is the emotion engine's analysis results, and the output is a tailored response or information delivery method.

[0333] Step 11:

[0334] The server accumulates the emotion data obtained from the emotion engine as feedback and reflects it in future system improvements. The input is emotion analysis feedback data, and the output is future system optimization.

[0335] Through these steps, the system enables efficient uploading, analysis, improvement, sharing, and searching of business knowledge data, as well as flexible information provision based on user sentiment.

[0336] (Application example 2)

[0337] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] Current factory robot management systems struggle to provide appropriate information in real time during maintenance and troubleshooting. Furthermore, they are unable to properly analyze the stress and frustration felt by users and provide appropriate support, resulting in a decline in user efficiency and satisfaction. In particular, there is a need to effectively utilize the large volume of business knowledge documents and quickly search and provide relevant information.

[0339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0340] In this invention, the server includes means for uploading business knowledge documents, means having a generative AI model for analyzing the uploaded documents, means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals, means for automatically categorizing the analyzed documents and storing them in a database, means for providing related information from the stored documents based on a user's search query, means for instantly responding to user questions in a chat format, and means for analyzing user emotions and adapting a service provision method based on the user's emotions. This enables effective management and search of business knowledge documents, provision of real-time support, and optimal responses according to user emotions.

[0341] "Business knowledge documents" refer to documents that contain business-related knowledge, procedures, data, etc.

[0342] "Upload" refers to a user sending a file from a local environment to a server.

[0343] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and performs text generation and analysis for specific tasks.

[0344] "Inconsistencies and unclear expressions" refer to contradictions in information within a document or expressions whose meaning is unclear.

[0345] "Specific improvement proposals" refer to proposals that show specific methods and content for correcting inconsistencies or unclear parts.

[0346] "Categorization" refers to classifying analyzed documents based on their content or characteristics.

[0347] A "database" refers to a system that allows information to be systematically stored, managed, and searched.

[0348] A "search query" refers to text that a user enters to search for specific information.

[0349] "Chat style" refers to an interface that provides information in an interactive format using text or voice.

[0350] "Immediate response" refers to replying to inquiries from users in real time.

[0351] "Emotion analysis" refers to analyzing a user's emotional state from text input or voice data.

[0352] "Adapting the service delivery method based on the user's emotions" refers to providing appropriate information and responses according to the user's emotional state based on the results of emotion analysis.

[0353] As an embodiment of the present invention, a system specialized for managing and maintaining factory robots will be developed. Each element of the system and its operation will be specifically described below.

[0354] System configuration

[0355] The system uses the following hardware and software:

[0356] Server: Parses, stores, and searches content (e.g., AWS EC2)

[0357] Devices: Smartphones and tablets are used (interfaces for administrators and staff)

[0358] Robots: Robots operating in factories (e.g. ABB, FANUC)

[0359] Generative AI models: Algorithms that analyze content, improve it, and generate responses (e.g., GPT-3)

[0360] Emotion engine: An engine for analyzing user emotions (e.g., EmotionAnalyzer)

[0361] Database: A system for storing and managing business knowledge documents (e.g., MySQL)

[0362] System Operation

[0363] 1. Uploading and analyzing business knowledge documents

[0364] Users upload business knowledge documents to the system using devices such as smartphones and tablets. The uploaded documents are sent to a server and analyzed by a generative AI model. This analysis detects inconsistencies and unclear expressions.

[0365] 2. Peer review and improvement of knowledge

[0366] The server receives the results of the document analysis using the generative AI model and generates specific improvement proposals. For example, it detects incorrect data entry or unclear explanations and creates improvement proposals to correct them. The improvement proposals are reflected in the document as new entries and are then saved again.

[0367] 3. Information structuring and automatic categorization

[0368] The server automatically categorizes documents analyzed by the generative AI model, classifying document content into specific categories and adding relevant tags, storing structured data in a database for future searches.

[0369] 4. Knowledge sharing and discovery

[0370] Users can use their terminals to access the system's search interface and search for the information they need, for example, by searching for "robot maintenance procedures." The server then extracts relevant business knowledge documents from the database and provides them to the user.

[0371] 5. Chat support

[0372] Users can use the system's chat interface to ask questions in real time. For example, "What should I do if my machine is behaving abnormally?" The server uses a generative AI model to analyze the question and generate the optimal answer. The generated answer is immediately displayed in the chat window, allowing users to quickly obtain the information they need.

[0373] 6. Use of Emotion Engines

[0374] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will recognize this and adjust the system to provide a kind response.

[0375] Specific examples

[0376] A factory manager can upload a machine maintenance manual using a smartphone. The system detects any unclear areas and uses a generative AI model to improve them. For example, the system might prompt the user to "scan this document and improve the unclear sections." The improvements and categories are then saved in a database, allowing for quicker response if a similar issue arises in the future.

[0377] In addition, when an administrator searches for "robot maintenance procedures" in the search interface, the system provides relevant information. When an administrator asks "What should I do if my machine is behaving abnormally?" in the chat interface, the system provides the most appropriate answer. By also using an emotion engine, it becomes possible to provide information that takes into account the user's emotions.

[0378] In this way, a system is realized that allows for efficient and effective management and maintenance of the factory.

[0379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0380] Step 1:

[0381] A user uploads a business knowledge document using a terminal. Specifically, the user selects a file from a smartphone or tablet and presses the upload button, which sends the document to the server. The input at this time is the document file selected by the user, and the output is the document data saved on the server.

[0382] Step 2:

[0383] The server passes the uploaded document to a generative AI model that analyzes it. The generative AI model scans the document's contents to detect inconsistencies and ambiguous expressions. The input is the document data stored on the server, and the output is the analysis results (a list of inconsistencies and ambiguous expressions).

[0384] Step 3:

[0385] The server generates specific improvement proposals based on the analysis results obtained by the generative AI model. Based on the analysis results, the AI ​​model generates specific methods and content for correcting inconsistencies and unclear parts of the document. The input is the analysis results, and the output is a document that reflects the improvement proposals.

[0386] Step 4:

[0387] The server automatically categorizes the improved documents and generates relevant tags. The generated documents are classified into specific categories and have tags added to them for future searches. The input is the document with the proposed improvements, and the output is structured data with categories and tags.

[0388] Step 5:

[0389] The server stores the structured data with categories and tags in a database, which can then be quickly served to users for future searches. The input is documents with categories and tags, and the output is the document data stored in the database.

[0390] Step 6:

[0391] A user accesses the search interface using a terminal and searches for the information they need. The server extracts and provides relevant documents from the database based on the user's search query. The input is the user's search query, and the output is the relevant documents provided as search results.

[0392] Step 7:

[0393] Users input questions in real time using the system's chat interface. The server analyzes the user's question using a generative AI model and generates an optimal answer. The input is the user's question, and the output is the answer generated by the generative AI model.

[0394] Step 8:

[0395] The server passes the user's input text or voice data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the user's emotional state and adapts the service delivery method based on the results. The input is the user's text or voice data, and the output is the emotion analysis results and a response method based on that.

[0396] For example, when an administrator inputs the prompt "Scan this document and improve the unclear sections," the generative AI model identifies the unclear areas and generates specific improvement suggestions. The improvements are saved in the database with categories and tags, and are quickly made available to users in response to searches or inquiries.

[0397] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0398] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0403] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0405] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0407] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0408] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0409] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0410] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0412] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0413] MODE FOR CARRYING OUT THE INVENTION

[0414] This invention is a system for automatically storing and effectively utilizing business knowledge. This system uses a generative AI model to review, improve, and appropriately categorize business knowledge documents, allowing users to smoothly obtain the information they need.

[0415] Document upload and parsing

[0416] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0417] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0418] Knowledge peer review and improvement

[0419] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0420] Information structuring and automatic categorization

[0421] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0422] Knowledge sharing and discovery

[0423] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0424] Chat-style support

[0425] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0426] The above is an embodiment of the present invention. This system makes it possible to efficiently store and share business knowledge and provide an environment in which users can quickly and accurately obtain the information they need.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] A user uploads a business knowledge document to the system using a terminal. The user opens a browser and accesses the system's upload page. Then, the user clicks the "Select File" button to select the document file and clicks the "Upload" button.

[0430] Step 2:

[0431] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0432] Step 3:

[0433] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0434] Step 4:

[0435] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0436] Step 5:

[0437] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0438] Step 6:

[0439] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0440] Step 7:

[0441] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0442] Step 8:

[0443] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0444] Step 9:

[0445] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0446] Step 10:

[0447] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[0448] Step 11:

[0449] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[0450] Step 12:

[0451] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[0452] Through these steps, the system enables efficient storage and sharing of business knowledge, providing an environment in which users can quickly and accurately obtain the information they need.

[0453] Example 1

[0454] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0455] The vast volume and complexity of business knowledge data makes it difficult to efficiently organize it and quickly obtain the necessary information. Furthermore, the manual correction of data containing inconsistencies or unclear expressions takes time and effort, so there is a need to improve efficiency.

[0456] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0457] In this invention, the server includes: means for a user to upload business knowledge data using an information processing device; means for receiving the uploaded data and passing it to a generative AI model as an analysis target; means for analyzing the data using the generative AI model to detect inconsistencies and unclear expressions and generate specific improvement proposals; means for automatically categorizing the analyzed data, adding associated tags, and saving the data as structured data in a database; means for quickly providing related information from the saved data based on a user's search query; and means for generating answers to user questions in real time using the generative AI model and providing immediate responses in chat format. This allows business knowledge data to be organized efficiently, making it possible to quickly and accurately obtain necessary information.

[0458] A "user" is an individual or organization that uses the system to upload and search business knowledge data.

[0459] An "information processing device" is a device that a user uses to upload business knowledge data, and includes a personal computer, a tablet, a smartphone, and the like.

[0460] "Business knowledge data" refers to documents, files, or data sets that contain business-related knowledge or information.

[0461] The "upload interface" is an online interface that allows users to send business knowledge data to the server.

[0462] A "generative AI model" is an artificial intelligence model that analyzes received data, detects inconsistencies and unclear expressions, and generates specific improvement proposals.

[0463] The "server" is a central computer system that receives, stores, and analyzes uploaded business knowledge data, drives generative AI models, and executes various processes.

[0464] "Analysis target" refers to the data that will be analyzed by the generative AI model after being uploaded to the server.

[0465] An "inconsistency" is an area in the data where there is a lack of consistency or contradiction.

[0466] "Unclear expression" means an expression in which the data content is vague and difficult to understand.

[0467] "Specific improvement suggestions" are correction suggestions automatically generated by the generative AI model to resolve inconsistencies or unclear expressions.

[0468] "Categorization" is the process of classifying analyzed data into specific categories.

[0469] "Tags" are additional information that add highly relevant keywords to data to make it easier to search for later.

[0470] A "database" is a digital storage device for storing structured data that has been analyzed and archived.

[0471] A "search query" is a keyword or phrase that a user enters to search for desired information.

[0472] "Chat format" means that users input questions in real time and the system responds immediately.

[0473] "Generating answers in real time" means automatically and quickly generating answers immediately after a user enters a question.

[0474] MODE FOR CARRYING OUT THE INVENTION

[0475] This invention is a system for efficiently managing business knowledge data and enabling users to quickly and accurately obtain the information they need. This system uses a generative AI model to analyze business knowledge data and automatically detect and correct data inconsistencies and unclear expressions. It also automatically categorizes the analyzed data, adds tags, and stores it in a database as structured data, providing an environment that makes it easy for users to search.

[0476] Uploading and saving documents

[0477] A user uploads business knowledge data using an information processing device (e.g., a PC or tablet). The user selects a file through the system's upload interface and sends it to the system. For example, if a user uploads an Excel file (e.g., excel_file.xlsx), the file is sent to the server and stored in the database along with metadata (upload date and time, file size, etc.).

[0478] Document Parsing

[0479] The server passes the stored document to a generative AI model for analysis. The generative AI model scans the document for inconsistencies and unclear expressions. For example, it reads the contents of each cell in an Excel spreadsheet and checks for inconsistencies.

[0480] Knowledge peer review and improvement

[0481] The server receives the analysis results of the generative AI model and automatically generates specific improvement proposals for detected inconsistencies and unclear expressions. For example, to a cell that simply says "Sales forecast," add a detailed explanation such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry.

[0482] Information structuring and automatic categorization

[0483] The server automatically categorizes the analyzed documents using the generative AI model. For example, a document related to "project management" would be classified under the "project management" category. Additionally, related tags such as "budget planning" and "marketing strategy" are automatically added as needed and saved in the database.

[0484] Knowledge sharing and discovery

[0485] A user accesses the system's search interface using a terminal and enters a query, for example, "marketing strategy for a new product," into the search box. The server then quickly retrieves relevant documents from the database and displays them to the user. The retrieved documents are presented in a list format in order of relevance.

[0486] Chat-style support

[0487] A user can use the system's chat interface to input a question. For example, "How do I create a budget plan for next year?" The server analyzes this question using a generative AI model and automatically generates an appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to review its contents and obtain the necessary information.

[0488] Specific operation example

[0489] Upload: The user uploads an Excel file (excel_file.xlsx) from their computer. The server receives and saves this file.

[0490] Analysis: The server passes the file to the generative AI model for analysis. For example, if cell B4 contains only "Sales Forecast," the generative AI model will detect the inconsistency.

[0491] Improvement: The server adds a specific improvement proposal: "Sales forecast (Q1 FY2023) - Estimated amount: 5 million yen."

[0492] Categorize: The server categorizes the file into the "Project Management" category and adds the "Budget Planning" tag.

[0493] Search: When a user searches for "marketing strategy for a new product," relevant documents are displayed.

[0494] Chat support: When a user types, "How do I create a budget plan for next year?", the server instantly generates and displays the appropriate answer.

[0495] Prompt Sentence Examples

[0496] Below are some examples of prompt sentences.

[0497] Document Upload: "Upload your business knowledge data. The system will analyze the data and detect inconsistencies or unclear wording."

[0498] Improved analysis results: "The generative AI model detected the following unclear areas and provided specific suggestions for improvement."

[0499] Enter a search query: "Enter 'marketing strategy for a new product' in the system's search interface. Relevant documents will be displayed."

[0500] Chat support example: "Type 'How do I create a budget plan for next year?' into the chat interface."

[0501] The above is an embodiment of the present invention. By using this system, business knowledge data can be efficiently organized, and necessary information can be obtained quickly and accurately.

[0502] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0503] Step 1:

[0504] A user uploads business knowledge data (e.g., excel_file.xlsx) using an information processing device. The user clicks the "Select File" button in the system's upload interface, selects a file, and presses the upload button. The input here is the business knowledge data, and the output is the data sent to the server.

[0505] Step 2:

[0506] The server receives the uploaded data and stores it in a database along with metadata (upload date and time, file size, etc.). At this time, the server prepares the received data to be passed to the generative AI model for analysis. The input is the uploaded business knowledge data, and the output is the stored data.

[0507] Step 3:

[0508] The server passes the stored data to the generative AI model, which then begins analysis. The generative AI model scans the data to detect inconsistencies and unclear expressions. In this case, the input is business knowledge data, and the output is the analysis results. For example, each cell in an Excel spreadsheet is read one by one, and the contents are checked for inconsistencies.

[0509] Step 4:

[0510] The server automatically generates specific improvement proposals based on the analysis results obtained from the generative AI model. For example, for a cell that simply says "Sales forecast," it generates a detailed improvement proposal such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry. The input here is the analysis result from the generative AI model, and the output is a new document that reflects the improvement proposal.

[0511] Step 5:

[0512] The server automatically categorizes the data analyzed and improved by the generative AI model. For example, data related to "project management" is classified into the "project management" category and related tags such as "budget planning" and "marketing strategy" are automatically added. The input is a document with the proposed improvements reflected, and the output is categorized and tagged structured data.

[0513] Step 6:

[0514] The server stores the categorized data in a database. The data is stored as structured data with category and tag information, making it easier to search later. The input is data with categories and tags, and the output is the data stored in the database.

[0515] Step 7:

[0516] A user accesses the system's search interface using a terminal and enters a search query, for example, "marketing strategy for a new product" in the search box. The server retrieves relevant information from the database based on the entered query and displays it to the user. Here, the input is the search query, and the output is a list of relevant documents.

[0517] Step 8:

[0518] A user enters a question into the system's chat interface. For example, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate an appropriate answer. The generated answer is immediately displayed in the chat window. The input here is the user's question, and the output is the answer from the generative AI model.

[0519] The above are the specific processing steps of this system. The specific operations performed at each step are necessary to achieve efficient management of business knowledge data and rapid information acquisition.

[0520] (Application example 1)

[0521] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0522] Conventional business knowledge management systems have problems with inefficient document uploading, analysis, improvement, categorization, search, and real-time response. Especially in large-scale business environments such as logistics centers, manual business knowledge management is time-consuming, labor-intensive, and inefficient. Furthermore, there is no established method for utilizing general-purpose computing devices such as smartphones, making it difficult to respond immediately on-site.

[0523] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0524] In this invention, the server includes: means for uploading business knowledge documents; means for having a generative AI model that analyzes the uploaded documents; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed documents and saving them in a database; means for providing related information from the saved documents based on a user's search query; means for instantly responding to user questions in chat format; means for scanning and uploading business knowledge documents via a smartphone or general-purpose computing device; and means for classifying the business knowledge documents analyzed by the generative AI model into categories such as project management, quality control, and inventory management. This enables efficient management of business knowledge, rapid on-site response, and appropriate categorization.

[0525] A "business knowledge document" is a document that contains knowledge and information about business in a company or organization.

[0526] "Document upload" is an operation in which a user sends their own electronic files or scanned paper documents to the system.

[0527] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate improvement suggestions based on the analysis results.

[0528] "Inconsistencies and unclear expressions" refers to conflicting information or parts of a document that are difficult to understand.

[0529] "Specific improvement proposals" are proposals based on the analysis results to make the content of the document clearer and more accurate.

[0530] "Automatic categorization" is the process of classifying analyzed documents into appropriate categories using a generative AI model.

[0531] A "database" is a data storage system that structures and stores parsed documents and other related information.

[0532] A "search query" is a keyword or phrase that a user enters into a system to retrieve desired information.

[0533] "Chat-style instant response" is an interface that provides answers to questions from users in real time.

[0534] A "smartphone or general-purpose computing device" is a portable electronic device or general-purpose computer used to perform operations such as scanning, uploading, and searching.

[0535] "Generative AI categorization" is the process by which a generative AI model analyzes the content of a document and appropriately classifies it into categories such as project management, quality control, or inventory management.

[0536] The system described in this invention, "LogiMaster," efficiently manages business knowledge documents and is intended for use in logistics centers in particular. This system is realized using the following hardware and software.

[0537] Hardware used

[0538] Server: A central computing device that stores, analyzes, and processes documents.

[0539] Smartphone or general-purpose computing device: A handheld device or general-purpose computer for scanning documents, uploading documents, entering search queries, and chatting.

[0540] Software used

[0541] Flask: Used as a web framework to provide the user interface and API endpoints.

[0542] OpenAI API: Responsible for analyzing documents with generative AI models and generating improvement suggestions.

[0543] SQLAlchemy: An ORM library for database management that uses SQLite as a backend.

[0544] Data processing and calculation

[0545] The server receives business knowledge documents uploaded by users and analyzes and proposes improvements through a generative AI model. Documents uploaded using smartphones or general-purpose computing devices are processed as follows:

[0546] Document upload: Users can use their smartphone camera to scan paper documents or directly upload existing electronic files.

[0547] Analysis and improvement suggestions: Documents sent to the server are analyzed by a generative AI model using the OpenAI API to detect inconsistencies and unclear expressions and suggest specific improvement suggestions.

[0548] Categorization and database storage: Parsed documents are automatically categorized into categories such as project management, quality control, inventory management, etc. and stored in a database using SQLAlchemy.

[0549] Search and chat functionality: When a user enters a search query, the server extracts and provides relevant information from stored documents, and a generative AI model responds immediately to the user's questions using a real-time chat interface.

[0550] Specific examples

[0551] For example, if a logistics center manager wants to search for "new ways to optimize inventory procedures," he or she can enter the following query on a smartphone:

[0552] How can you optimize your new receiving procedures?

[0553] Based on this query, the server uses a generative AI model to search for relevant business knowledge documents and provide the most appropriate information.

[0554] A specific example of a prompt sentence for a generative AI model is as follows:

[0555] Please analyze the following business knowledge document and suggest improvements:

[0556] ---

[0557] Document Title: "Logistics Center Financial Report"

[0558] 1. Consider ways to reduce inventory costs

[0559] 2. Improving work efficiency

[0560] 3. Automation of work through mechanization

[0561] 4. ...

[0562] Please analyze and suggest improvements.

[0563] In this way, the system based on the present invention efficiently manages business knowledge and provides support for quickly obtaining required information.

[0564] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0565] Step 1:

[0566] A user uploads a business knowledge document using a smartphone or general-purpose computing device. Specifically, the user scans a paper document with the smartphone camera or selects an existing electronic file and presses the upload button. The input data is the document file, and the output is the data sent to the server.

[0567] Step 2:

[0568] The server saves the uploaded document. Specifically, it receives the document file using Flask and saves it in a specific folder on the server. The input data is the data sent by the user, and the output is a file saved on the server.

[0569] Step 3:

[0570] The server passes the saved document to the generative AI model for analysis. Specifically, it uses the OpenAI API to send the document content to the analysis model in the form of a prompt. The input data is the saved document content, and the output is the analysis result of the generative AI model.

[0571] Step 4:

[0572] The generative AI model detects inconsistencies and unclear expressions in the document and proposes specific improvement suggestions. Specifically, the generative AI model extracts improvement suggestions from the analysis results it provides and adds them to the document as new comments or corrections. The input data are the analysis results, and the output is a new document containing the improvement suggestions.

[0573] Step 5:

[0574] The server automatically categorizes documents analyzed by the generative AI model, specifically assigning categories such as project management, quality control, and inventory management based on the document content. The input data is the refined document content, and the output is the classified category.

[0575] Step 6:

[0576] The server stores the categorized documents in a database. Specifically, it uses SQLAlchemy to store the document content and category information in the database. The input data is the document with classification information, and the output is the saved state of the database.

[0577] Step 7:

[0578] A user inputs a search query using a smartphone or a general-purpose computing device. Specifically, the user inputs keywords into a search interface and presses a search button. The input data is the search query, and the output is a request for search results.

[0579] Step 8:

[0580] The server searches for and provides relevant documents in the database based on the search query. Specifically, it uses SQLAlchemy to extract documents that match the query and display them to the user. The input data is the user's search query, and the output is a list of relevant documents.

[0581] Step 9:

[0582] A user inputs a question in real time using the chat interface. Specifically, the user inputs a question in the chat window and presses the send button. The input data is a chat-style question, and the output is a request for an immediate response.

[0583] Step 10:

[0584] The server uses a generative AI model to respond to the user's questions immediately. Specifically, the input question is passed to the AI ​​model as a prompt, and the generated answer is displayed to the user. The input data is the question, and the output is the answer from the generative AI model.

[0585] Through these steps, efficient management of business knowledge and rapid response in the field will be achieved.

[0586] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0587] MODE FOR CARRYING OUT THE INVENTION

[0588] This invention is a system for automatically storing and effectively utilizing business knowledge. This system combines a generative AI model and an emotion engine to review, refine, categorize, and search business knowledge documents, and provides information based on user emotions.

[0589] Document upload and parsing

[0590] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0591] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0592] Knowledge peer review and improvement

[0593] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0594] Information structuring and automatic categorization

[0595] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0596] Knowledge sharing and discovery

[0597] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0598] Chat-style support

[0599] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0600] Use of emotion engine

[0601] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[0602] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[0603] The server can also store the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements, allowing the system to provide more appropriate information according to the user's emotional state.

[0604] In this way, the present invention utilizes generative AI models and emotion engines to enable efficient storage, sharing, and search of business knowledge, as well as optimization of user experience.

[0605] The processing flow will be explained below.

[0606] Step 1:

[0607] A user uploads a business knowledge document to the system using a terminal. For example, the user opens a browser, accesses the system's upload page, clicks the "Select File" button, selects the document file, and clicks the "Upload" button.

[0608] Step 2:

[0609] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0610] Step 3:

[0611] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0612] Step 4:

[0613] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0614] Step 5:

[0615] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0616] Step 6:

[0617] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0618] Step 7:

[0619] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0620] Step 8:

[0621] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0622] Step 9:

[0623] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0624] Step 10:

[0625] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[0626] Step 11:

[0627] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[0628] Step 12:

[0629] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[0630] Step 13:

[0631] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface, the tone of their search queries, etc.

[0632] Step 14:

[0633] The emotion engine analyzes the user's emotional state and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[0634] Step 15:

[0635] The server accumulates the emotional data obtained from the emotion engine as feedback and reflects it in future system improvements, enabling the system to provide more appropriate information according to the user's emotional state.

[0636] Through these steps, the system enables efficient storage, sharing, and search of business knowledge, as well as the provision of information based on user emotions.

[0637] Example 2

[0638] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] In conventional business knowledge management systems, storing and searching knowledge documents is done manually, requiring a great deal of time and effort. Furthermore, detecting inconsistencies and unclear expressions and proposing improvements is manual, making efficient improvements difficult. Furthermore, information is not provided based on the user's emotional state, resulting in low user convenience and satisfaction. There was a need to provide an efficient and flexible business knowledge management system that could solve these issues.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0641] In this invention, the server includes: means for uploading business knowledge data; means having a generative AI model for analyzing the uploaded data; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed data and saving it in a database; means for providing related information from the saved data based on a user's search query; means for instantly responding to user questions in chat format; means for passing real-time input data from the user to an emotion engine and analyzing the emotional state; and means for adapting system operation based on the analysis results of the emotion engine and accumulating feedback. This enables efficient storage, analysis, improvement, and sharing of business knowledge data, and optimal information provision based on the user's emotional state.

[0642] "Business knowledge data" is electronic data that records knowledge and information related to business.

[0643] "Uploading means" is a function that allows a user to send data from their own terminal to the server.

[0644] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and suggest improvements.

[0645] "Means for detecting inconsistencies and ambiguities" refers to the ability to use generative AI models to find errors or ambiguities in the data.

[0646] The "means for generating specific improvement proposals" is a function that automatically suggests appropriate correction methods for detected inconsistencies or unclear expressions.

[0647] "Means for automatically categorizing analyzed data and storing it in a database" refers to a function that classifies analyzed data based on its meaning, tags it appropriately, and stores it in a database.

[0648] "Means for providing relevant information from stored data based on a user's search query" refers to a function that extracts and provides appropriate information from a database based on search keywords entered by the user.

[0649] The "means for immediate response in chat format" is a function that responds in real time to questions entered by the user through a chat interface.

[0650] An "emotion engine" is an algorithm that analyzes a user's text input and behavioral data to understand the user's emotional state.

[0651] The "means for passing user input data to the emotion engine and analyzing the emotional state" is a function for sending user input information to the emotion engine and analyzing the user's emotions based on the results.

[0652] "Means for adapting the system's behavior based on the analysis results of the emotion engine" is a function that adjusts the system's information provision method and response content based on the user's emotional state.

[0653] "Means for accumulating feedback" is a function that records the analysis results of the emotion engine and user reactions in the system and uses them for future improvements.

[0654] This invention is a system that combines a generative AI model and an emotion engine to efficiently manage, analyze, improve, share, and search business knowledge data, and provide information based on user emotions as appropriate. A specific embodiment of this system will be described below.

[0655] System configuration

[0656] The system includes the following major hardware and software components:

[0657] 1. Server: The central hardware for data processing and storage.

[0658] 2. Terminal: A device (such as a personal computer or smartphone) that provides an interface for users to access the system.

[0659] 3. Generative AI model: A software algorithm that analyzes business knowledge data and generates specific improvement proposals.

[0660] 4. Emotion engine: An algorithm that analyzes user input data and adjusts the system's behavior based on the user's emotional state.

[0661] Program processing

[0662] Document upload and parsing

[0663] Users use their terminals to upload their own business knowledge data to the system. For example, a user selects a business report (Excel file) on their computer and uploads the file through the system's upload interface. At this stage, the data is sent to the server and saved.

[0664] The server passes the received data to a generative AI model for analysis. The generative AI model scans the document content to detect inconsistencies and unclear expressions. The specific software used is an analysis tool that uses natural language processing (NLP).

[0665] Knowledge peer review and improvement

[0666] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the data. For example, it detects incorrect data input or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the data as new entries.

[0667] Information structuring and automatic categorization

[0668] The server automatically categorizes the data analyzed by the generative AI model. For example, if the data content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores the data in a database as structured data for future searches.

[0669] Knowledge sharing and discovery

[0670] Users use their devices to access the system's search interface and search for the information they need. For example, they enter a search query like "marketing strategy for a new product." The server quickly extracts relevant data from its stock database and presents it to the user, allowing them to smoothly access the information they need.

[0671] Chat-style support

[0672] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0673] Use of emotion engine

[0674] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[0675] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more friendly and detailed explanation. The server can also accumulate the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements.

[0676] Examples and prompts

[0677] For example:

[0678] 1. Document upload: "A user uploads an Excel file of a sales report from their computer to the system."

[0679] 2. Information search: "The user types 'tell me about the latest technology trends' into the search box, and the server provides the latest relevant technology reports."

[0680] 3. Chat support: "Users can type 'how to set up cloud storage' into the chat window, and the system will prompt them with the setup instructions."

[0681] Example prompts to input to a generative AI model:

[0682] Please analyze the sales report.

[0683] Tell us about the latest technology trends.

[0684] How do I set up cloud storage?

[0685] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0686] The flow of this system's program processing

[0687] Step 1:

[0688] Users use their terminals to upload business knowledge data to the system. For example, users select a business report (Excel file) on their terminal and upload the file through the system's upload interface. The input is the user's business knowledge file, and the output is that the file is sent to the server and saved.

[0689] Step 2:

[0690] The server passes the uploaded data to a generative AI model, which analyzes the content of the data and detects inconsistencies and unclear expressions. For example, the server passes the data through an NLP (natural language processing) tool to perform word frequency and context analysis. The input is business knowledge data, and the output is a list of inconsistencies and unclear expressions as a result of the analysis.

[0691] Step 3:

[0692] The server automatically generates specific improvement proposals based on the analysis results received from the generative AI model. For example, the generative AI model has the function of presenting "inconsistent data" and "correct data examples." The input is the analysis results of the generative AI model, and the output is new knowledge data with improvement proposals added.

[0693] Step 4:

[0694] The server automatically categorizes the analyzed and improved data and stores it in a database. For example, it can be classified into categories such as "project management" or "marketing." The input is knowledge data with improvement suggestions added, and the output is categorized and stored in the database as structured data.

[0695] Step 5:

[0696] A user uses a terminal to access the system's search interface and search for the information they need. For example, they enter a keyword such as "marketing strategy for a new product." The input is the user's search query, and the output is the relevant information in the database.

[0697] Step 6:

[0698] The server extracts relevant information from the database based on the search query and provides it to the user. For example, the server filters the appropriate data and displays the top results. The input is the user's search query, and the output is the filtered relevant information.

[0699] Step 7:

[0700] The user uses the chat interface of the system to input a question in real time, for example, "How do I create a budget plan for next year?" The input is the user's real-time question, and the output is the question being sent to the server.

[0701] Step 8:

[0702] The server uses a generative AI model to analyze the user's question and generate the optimal answer. For example, the generative AI model refers to past data to suggest an appropriate answer. The input is the user's question data, and the output is the generated answer.

[0703] Step 9:

[0704] The server instantly displays the generated response in the chat window for the user. At this time, the emotion engine analyzes the user's text input and determines their emotional state. The input is the response from the generative AI model and the user's text input, and the output is an optimized response and feedback of the emotional state.

[0705] Step 10:

[0706] Based on the emotion analysis results from the emotion engine, the server adjusts the system's behavior. For example, if the user is feeling frustrated, the system will provide a detailed and helpful explanation. The input is the emotion engine's analysis results, and the output is a tailored response or information delivery method.

[0707] Step 11:

[0708] The server accumulates the emotion data obtained from the emotion engine as feedback and reflects it in future system improvements. The input is emotion analysis feedback data, and the output is future system optimization.

[0709] Through these steps, the system enables efficient uploading, analysis, improvement, sharing, and searching of business knowledge data, as well as flexible information provision based on user sentiment.

[0710] (Application example 2)

[0711] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] Current factory robot management systems struggle to provide appropriate information in real time during maintenance and troubleshooting. Furthermore, they are unable to properly analyze the stress and frustration felt by users and provide appropriate support, resulting in a decline in user efficiency and satisfaction. In particular, there is a need to effectively utilize the large volume of business knowledge documents and quickly search and provide relevant information.

[0713] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0714] In this invention, the server includes means for uploading business knowledge documents, means having a generative AI model for analyzing the uploaded documents, means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals, means for automatically categorizing the analyzed documents and storing them in a database, means for providing related information from the stored documents based on a user's search query, means for instantly responding to user questions in a chat format, and means for analyzing user emotions and adapting a service provision method based on the user's emotions. This enables effective management and search of business knowledge documents, provision of real-time support, and optimal responses according to user emotions.

[0715] "Business knowledge documents" refer to documents that contain business-related knowledge, procedures, data, etc.

[0716] "Upload" refers to a user sending a file from a local environment to a server.

[0717] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and performs text generation and analysis for specific tasks.

[0718] "Inconsistencies and unclear expressions" refer to contradictions in information within a document or expressions whose meaning is unclear.

[0719] "Specific improvement proposals" refer to proposals that show specific methods and content for correcting inconsistencies or unclear parts.

[0720] "Categorization" refers to classifying analyzed documents based on their content or characteristics.

[0721] A "database" refers to a system that allows information to be systematically stored, managed, and searched.

[0722] A "search query" refers to text that a user enters to search for specific information.

[0723] "Chat style" refers to an interface that provides information in an interactive format using text or voice.

[0724] "Immediate response" refers to replying to inquiries from users in real time.

[0725] "Emotion analysis" refers to analyzing a user's emotional state from text input or voice data.

[0726] "Adapting the service delivery method based on the user's emotions" refers to providing appropriate information and responses according to the user's emotional state based on the results of emotion analysis.

[0727] As an embodiment of the present invention, a system specialized for managing and maintaining factory robots will be developed. Each element of the system and its operation will be specifically described below.

[0728] System configuration

[0729] The system uses the following hardware and software:

[0730] Server: Parses, stores, and searches content (e.g., AWS EC2)

[0731] Devices: Smartphones and tablets are used (interfaces for administrators and staff)

[0732] Robots: Robots operating in factories (e.g. ABB, FANUC)

[0733] Generative AI models: Algorithms that analyze content, improve it, and generate responses (e.g., GPT-3)

[0734] Emotion engine: An engine for analyzing user emotions (e.g., EmotionAnalyzer)

[0735] Database: A system for storing and managing business knowledge documents (e.g., MySQL)

[0736] System Operation

[0737] 1. Uploading and analyzing business knowledge documents

[0738] Users upload business knowledge documents to the system using devices such as smartphones and tablets. The uploaded documents are sent to a server and analyzed by a generative AI model. This analysis detects inconsistencies and unclear expressions.

[0739] 2. Peer review and improvement of knowledge

[0740] The server receives the results of the document analysis using the generative AI model and generates specific improvement proposals. For example, it detects incorrect data entry or unclear explanations and creates improvement proposals to correct them. The improvement proposals are reflected in the document as new entries and are then saved again.

[0741] 3. Information structuring and automatic categorization

[0742] The server automatically categorizes documents analyzed by the generative AI model, classifying document content into specific categories and adding relevant tags, storing structured data in a database for future searches.

[0743] 4. Knowledge sharing and discovery

[0744] Users can use their terminals to access the system's search interface and search for the information they need, for example, by searching for "robot maintenance procedures." The server then extracts relevant business knowledge documents from the database and provides them to the user.

[0745] 5. Chat support

[0746] Users can use the system's chat interface to ask questions in real time. For example, "What should I do if my machine is behaving abnormally?" The server uses a generative AI model to analyze the question and generate the optimal answer. The generated answer is immediately displayed in the chat window, allowing users to quickly obtain the information they need.

[0747] 6. Use of Emotion Engines

[0748] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will recognize this and adjust the system to provide a kind response.

[0749] Specific examples

[0750] A factory manager can upload a machine maintenance manual using a smartphone. The system detects any unclear areas and uses a generative AI model to improve them. For example, the system might prompt the user to "scan this document and improve the unclear sections." The improvements and categories are then saved in a database, allowing for quicker response if a similar issue arises in the future.

[0751] In addition, when an administrator searches for "robot maintenance procedures" in the search interface, the system provides relevant information. When an administrator asks "What should I do if my machine is behaving abnormally?" in the chat interface, the system provides the most appropriate answer. By also using an emotion engine, it becomes possible to provide information that takes into account the user's emotions.

[0752] In this way, a system is realized that allows for efficient and effective management and maintenance of the factory.

[0753] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0754] Step 1:

[0755] A user uploads a business knowledge document using a terminal. Specifically, the user selects a file from a smartphone or tablet and presses the upload button, which sends the document to the server. The input at this time is the document file selected by the user, and the output is the document data saved on the server.

[0756] Step 2:

[0757] The server passes the uploaded document to a generative AI model that analyzes it. The generative AI model scans the document's contents to detect inconsistencies and ambiguous expressions. The input is the document data stored on the server, and the output is the analysis results (a list of inconsistencies and ambiguous expressions).

[0758] Step 3:

[0759] The server generates specific improvement proposals based on the analysis results obtained by the generative AI model. Based on the analysis results, the AI ​​model generates specific methods and content for correcting inconsistencies and unclear parts of the document. The input is the analysis results, and the output is a document that reflects the improvement proposals.

[0760] Step 4:

[0761] The server automatically categorizes the improved documents and generates relevant tags. The generated documents are classified into specific categories and have tags added to them for future searches. The input is the document with the proposed improvements, and the output is structured data with categories and tags.

[0762] Step 5:

[0763] The server stores the structured data with categories and tags in a database, which can then be quickly served to users for future searches. The input is documents with categories and tags, and the output is the document data stored in the database.

[0764] Step 6:

[0765] A user accesses the search interface using a terminal and searches for the information they need. The server extracts and provides relevant documents from the database based on the user's search query. The input is the user's search query, and the output is the relevant documents provided as search results.

[0766] Step 7:

[0767] Users input questions in real time using the system's chat interface. The server analyzes the user's question using a generative AI model and generates an optimal answer. The input is the user's question, and the output is the answer generated by the generative AI model.

[0768] Step 8:

[0769] The server passes the user's input text or voice data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the user's emotional state and adapts the service delivery method based on the results. The input is the user's text or voice data, and the output is the emotion analysis results and a response method based on that.

[0770] For example, when an administrator inputs the prompt "Scan this document and improve the unclear sections," the generative AI model identifies the unclear areas and generates specific improvement suggestions. The improvements are saved in the database with categories and tags, and are quickly made available to users in response to searches or inquiries.

[0771] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0772] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0774] [Third embodiment]

[0775] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0776] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0777] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0778] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0779] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0780] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0781] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0782] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0783] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0784] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0785] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0786] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0787] MODE FOR CARRYING OUT THE INVENTION

[0788] This invention is a system for automatically storing and effectively utilizing business knowledge. This system uses a generative AI model to review, improve, and appropriately categorize business knowledge documents, allowing users to smoothly obtain the information they need.

[0789] Document upload and parsing

[0790] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0791] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0792] Knowledge peer review and improvement

[0793] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0794] Information structuring and automatic categorization

[0795] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0796] Knowledge sharing and discovery

[0797] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0798] Chat-style support

[0799] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0800] The above is an embodiment of the present invention. This system makes it possible to efficiently store and share business knowledge and provide an environment in which users can quickly and accurately obtain the information they need.

[0801] The processing flow will be explained below.

[0802] Step 1:

[0803] A user uploads a business knowledge document to the system using a terminal. The user opens a browser and accesses the system's upload page. Then, the user clicks the "Select File" button to select the document file and clicks the "Upload" button.

[0804] Step 2:

[0805] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0806] Step 3:

[0807] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0808] Step 4:

[0809] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0810] Step 5:

[0811] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0812] Step 6:

[0813] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0814] Step 7:

[0815] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0816] Step 8:

[0817] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0818] Step 9:

[0819] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0820] Step 10:

[0821] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[0822] Step 11:

[0823] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[0824] Step 12:

[0825] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[0826] Through these steps, the system enables efficient storage and sharing of business knowledge, providing an environment in which users can quickly and accurately obtain the information they need.

[0827] Example 1

[0828] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] The vast volume and complexity of business knowledge data makes it difficult to efficiently organize it and quickly obtain the necessary information. Furthermore, the manual correction of data containing inconsistencies or unclear expressions takes time and effort, so there is a need to improve efficiency.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0831] In this invention, the server includes: means for a user to upload business knowledge data using an information processing device; means for receiving the uploaded data and passing it to a generative AI model as an analysis target; means for analyzing the data using the generative AI model to detect inconsistencies and unclear expressions and generate specific improvement proposals; means for automatically categorizing the analyzed data, adding associated tags, and saving the data as structured data in a database; means for quickly providing related information from the saved data based on a user's search query; and means for generating answers to user questions in real time using the generative AI model and providing immediate responses in chat format. This allows business knowledge data to be organized efficiently, making it possible to quickly and accurately obtain necessary information.

[0832] A "user" is an individual or organization that uses the system to upload and search business knowledge data.

[0833] An "information processing device" is a device that a user uses to upload business knowledge data, and includes a personal computer, a tablet, a smartphone, and the like.

[0834] "Business knowledge data" refers to documents, files, or data sets that contain business-related knowledge or information.

[0835] The "upload interface" is an online interface that allows users to send business knowledge data to the server.

[0836] A "generative AI model" is an artificial intelligence model that analyzes received data, detects inconsistencies and unclear expressions, and generates specific improvement proposals.

[0837] The "server" is a central computer system that receives, stores, and analyzes uploaded business knowledge data, drives generative AI models, and executes various processes.

[0838] "Analysis target" refers to the data that will be analyzed by the generative AI model after being uploaded to the server.

[0839] An "inconsistency" is an area in the data where there is a lack of consistency or contradiction.

[0840] "Unclear expression" means an expression in which the data content is vague and difficult to understand.

[0841] "Specific improvement suggestions" are correction suggestions automatically generated by the generative AI model to resolve inconsistencies or unclear expressions.

[0842] "Categorization" is the process of classifying analyzed data into specific categories.

[0843] "Tags" are additional information that add highly relevant keywords to data to make it easier to search for later.

[0844] A "database" is a digital storage device for storing structured data that has been analyzed and archived.

[0845] A "search query" is a keyword or phrase that a user enters to search for desired information.

[0846] "Chat format" means that users input questions in real time and the system responds immediately.

[0847] "Generating answers in real time" means automatically and quickly generating answers immediately after a user enters a question.

[0848] MODE FOR CARRYING OUT THE INVENTION

[0849] This invention is a system for efficiently managing business knowledge data and enabling users to quickly and accurately obtain the information they need. This system uses a generative AI model to analyze business knowledge data and automatically detect and correct data inconsistencies and unclear expressions. It also automatically categorizes the analyzed data, adds tags, and stores it in a database as structured data, providing an environment that makes it easy for users to search.

[0850] Uploading and saving documents

[0851] A user uploads business knowledge data using an information processing device (e.g., a PC or tablet). The user selects a file through the system's upload interface and sends it to the system. For example, if a user uploads an Excel file (e.g., excel_file.xlsx), the file is sent to the server and stored in the database along with metadata (upload date and time, file size, etc.).

[0852] Document Parsing

[0853] The server passes the stored document to a generative AI model for analysis. The generative AI model scans the document for inconsistencies and unclear expressions. For example, it reads the contents of each cell in an Excel spreadsheet and checks for inconsistencies.

[0854] Knowledge peer review and improvement

[0855] The server receives the analysis results of the generative AI model and automatically generates specific improvement proposals for detected inconsistencies and unclear expressions. For example, to a cell that simply says "Sales forecast," add a detailed explanation such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry.

[0856] Information structuring and automatic categorization

[0857] The server automatically categorizes the analyzed documents using the generative AI model. For example, a document related to "project management" would be classified under the "project management" category. Additionally, related tags such as "budget planning" and "marketing strategy" are automatically added as needed and saved in the database.

[0858] Knowledge sharing and discovery

[0859] A user accesses the system's search interface using a terminal and enters a query, for example, "marketing strategy for a new product," into the search box. The server then quickly retrieves relevant documents from the database and displays them to the user. The retrieved documents are presented in a list format in order of relevance.

[0860] Chat-style support

[0861] A user can use the system's chat interface to input a question. For example, "How do I create a budget plan for next year?" The server analyzes this question using a generative AI model and automatically generates an appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to review its contents and obtain the necessary information.

[0862] Specific operation example

[0863] Upload: The user uploads an Excel file (excel_file.xlsx) from their computer. The server receives and saves this file.

[0864] Analysis: The server passes the file to the generative AI model for analysis. For example, if cell B4 contains only "Sales Forecast," the generative AI model will detect the inconsistency.

[0865] Improvement: The server adds a specific improvement proposal: "Sales forecast (Q1 FY2023) - Estimated amount: 5 million yen."

[0866] Categorize: The server categorizes the file into the "Project Management" category and adds the "Budget Planning" tag.

[0867] Search: When a user searches for "marketing strategy for a new product," relevant documents are displayed.

[0868] Chat support: When a user types, "How do I create a budget plan for next year?", the server instantly generates and displays the appropriate answer.

[0869] Prompt Sentence Examples

[0870] Below are some examples of prompt sentences.

[0871] Document Upload: "Upload your business knowledge data. The system will analyze the data and detect inconsistencies or unclear wording."

[0872] Improved analysis results: "The generative AI model detected the following unclear areas and provided specific suggestions for improvement."

[0873] Enter a search query: "Enter 'marketing strategy for a new product' in the system's search interface. Relevant documents will be displayed."

[0874] Chat support example: "Type 'How do I create a budget plan for next year?' into the chat interface."

[0875] The above is an embodiment of the present invention. By using this system, business knowledge data can be efficiently organized, and necessary information can be obtained quickly and accurately.

[0876] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] A user uploads business knowledge data (e.g., excel_file.xlsx) using an information processing device. The user clicks the "Select File" button in the system's upload interface, selects a file, and presses the upload button. The input here is the business knowledge data, and the output is the data sent to the server.

[0879] Step 2:

[0880] The server receives the uploaded data and stores it in a database along with metadata (upload date and time, file size, etc.). At this time, the server prepares the received data to be passed to the generative AI model for analysis. The input is the uploaded business knowledge data, and the output is the stored data.

[0881] Step 3:

[0882] The server passes the stored data to the generative AI model, which then begins analysis. The generative AI model scans the data to detect inconsistencies and unclear expressions. In this case, the input is business knowledge data, and the output is the analysis results. For example, each cell in an Excel spreadsheet is read one by one, and the contents are checked for inconsistencies.

[0883] Step 4:

[0884] The server automatically generates specific improvement proposals based on the analysis results obtained from the generative AI model. For example, for a cell that simply says "Sales forecast," it generates a detailed improvement proposal such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry. The input here is the analysis result from the generative AI model, and the output is a new document that reflects the improvement proposal.

[0885] Step 5:

[0886] The server automatically categorizes the data analyzed and improved by the generative AI model. For example, data related to "project management" is classified into the "project management" category and related tags such as "budget planning" and "marketing strategy" are automatically added. The input is a document with the proposed improvements reflected, and the output is categorized and tagged structured data.

[0887] Step 6:

[0888] The server stores the categorized data in a database. The data is stored as structured data with category and tag information, making it easier to search later. The input is data with categories and tags, and the output is the data stored in the database.

[0889] Step 7:

[0890] A user accesses the system's search interface using a terminal and enters a search query, for example, "marketing strategy for a new product" in the search box. The server retrieves relevant information from the database based on the entered query and displays it to the user. Here, the input is the search query, and the output is a list of relevant documents.

[0891] Step 8:

[0892] A user enters a question into the system's chat interface. For example, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate an appropriate answer. The generated answer is immediately displayed in the chat window. The input here is the user's question, and the output is the answer from the generative AI model.

[0893] The above are the specific processing steps of this system. The specific operations performed at each step are necessary to achieve efficient management of business knowledge data and rapid information acquisition.

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] Conventional business knowledge management systems have problems with inefficient document uploading, analysis, improvement, categorization, search, and real-time response. Especially in large-scale business environments such as logistics centers, manual business knowledge management is time-consuming, labor-intensive, and inefficient. Furthermore, there is no established method for utilizing general-purpose computing devices such as smartphones, making it difficult to respond immediately on-site.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0898] In this invention, the server includes: means for uploading business knowledge documents; means for having a generative AI model that analyzes the uploaded documents; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed documents and saving them in a database; means for providing related information from the saved documents based on a user's search query; means for instantly responding to user questions in chat format; means for scanning and uploading business knowledge documents via a smartphone or general-purpose computing device; and means for classifying the business knowledge documents analyzed by the generative AI model into categories such as project management, quality control, and inventory management. This enables efficient management of business knowledge, rapid on-site response, and appropriate categorization.

[0899] A "business knowledge document" is a document that contains knowledge and information about business in a company or organization.

[0900] "Document upload" is an operation in which a user sends their own electronic files or scanned paper documents to the system.

[0901] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate improvement suggestions based on the analysis results.

[0902] "Inconsistencies and unclear expressions" refers to conflicting information or parts of a document that are difficult to understand.

[0903] "Specific improvement proposals" are proposals based on the analysis results to make the content of the document clearer and more accurate.

[0904] "Automatic categorization" is the process of classifying analyzed documents into appropriate categories using a generative AI model.

[0905] A "database" is a data storage system that structures and stores parsed documents and other related information.

[0906] A "search query" is a keyword or phrase that a user enters into a system to retrieve desired information.

[0907] "Chat-style instant response" is an interface that provides answers to questions from users in real time.

[0908] A "smartphone or general-purpose computing device" is a portable electronic device or general-purpose computer used to perform operations such as scanning, uploading, and searching.

[0909] "Generative AI categorization" is the process by which a generative AI model analyzes the content of a document and appropriately classifies it into categories such as project management, quality control, or inventory management.

[0910] The system described in this invention, "LogiMaster," efficiently manages business knowledge documents and is intended for use in logistics centers in particular. This system is realized using the following hardware and software.

[0911] Hardware used

[0912] Server: A central computing device that stores, analyzes, and processes documents.

[0913] Smartphone or general-purpose computing device: A handheld device or general-purpose computer for scanning documents, uploading documents, entering search queries, and chatting.

[0914] Software used

[0915] Flask: Used as a web framework to provide the user interface and API endpoints.

[0916] OpenAI API: Responsible for analyzing documents with generative AI models and generating improvement suggestions.

[0917] SQLAlchemy: An ORM library for database management that uses SQLite as a backend.

[0918] Data processing and calculation

[0919] The server receives business knowledge documents uploaded by users and analyzes and proposes improvements through a generative AI model. Documents uploaded using smartphones or general-purpose computing devices are processed as follows:

[0920] Document upload: Users can use their smartphone camera to scan paper documents or directly upload existing electronic files.

[0921] Analysis and improvement suggestions: Documents sent to the server are analyzed by a generative AI model using the OpenAI API to detect inconsistencies and unclear expressions and suggest specific improvement suggestions.

[0922] Categorization and database storage: Parsed documents are automatically categorized into categories such as project management, quality control, inventory management, etc. and stored in a database using SQLAlchemy.

[0923] Search and chat functionality: When a user enters a search query, the server extracts and provides relevant information from stored documents, and a generative AI model responds immediately to the user's questions using a real-time chat interface.

[0924] Specific examples

[0925] For example, if a logistics center manager wants to search for "new ways to optimize inventory procedures," he or she can enter the following query on a smartphone:

[0926] How can you optimize your new receiving procedures?

[0927] Based on this query, the server uses a generative AI model to search for relevant business knowledge documents and provide the most appropriate information.

[0928] A specific example of a prompt sentence for a generative AI model is as follows:

[0929] Please analyze the following business knowledge document and suggest improvements:

[0930] ---

[0931] Document Title: "Logistics Center Financial Report"

[0932] 1. Consider ways to reduce inventory costs

[0933] 2. Improving work efficiency

[0934] 3. Automation of work through mechanization

[0935] 4. ...

[0936] Please analyze and suggest improvements.

[0937] In this way, the system based on the present invention efficiently manages business knowledge and provides support for quickly obtaining required information.

[0938] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0939] Step 1:

[0940] A user uploads a business knowledge document using a smartphone or general-purpose computing device. Specifically, the user scans a paper document with the smartphone camera or selects an existing electronic file and presses the upload button. The input data is the document file, and the output is the data sent to the server.

[0941] Step 2:

[0942] The server saves the uploaded document. Specifically, it receives the document file using Flask and saves it in a specific folder on the server. The input data is the data sent by the user, and the output is a file saved on the server.

[0943] Step 3:

[0944] The server passes the saved document to the generative AI model for analysis. Specifically, it uses the OpenAI API to send the document content to the analysis model in the form of a prompt. The input data is the saved document content, and the output is the analysis result of the generative AI model.

[0945] Step 4:

[0946] The generative AI model detects inconsistencies and unclear expressions in the document and proposes specific improvement suggestions. Specifically, the generative AI model extracts improvement suggestions from the analysis results it provides and adds them to the document as new comments or corrections. The input data are the analysis results, and the output is a new document containing the improvement suggestions.

[0947] Step 5:

[0948] The server automatically categorizes documents analyzed by the generative AI model, specifically assigning categories such as project management, quality control, and inventory management based on the document content. The input data is the refined document content, and the output is the classified category.

[0949] Step 6:

[0950] The server stores the categorized documents in a database. Specifically, it uses SQLAlchemy to store the document content and category information in the database. The input data is the document with classification information, and the output is the saved state of the database.

[0951] Step 7:

[0952] A user inputs a search query using a smartphone or a general-purpose computing device. Specifically, the user inputs keywords into a search interface and presses a search button. The input data is the search query, and the output is a request for search results.

[0953] Step 8:

[0954] The server searches for and provides relevant documents in the database based on the search query. Specifically, it uses SQLAlchemy to extract documents that match the query and display them to the user. The input data is the user's search query, and the output is a list of relevant documents.

[0955] Step 9:

[0956] A user inputs a question in real time using the chat interface. Specifically, the user inputs a question in the chat window and presses the send button. The input data is a chat-style question, and the output is a request for an immediate response.

[0957] Step 10:

[0958] The server uses a generative AI model to respond to the user's questions immediately. Specifically, the input question is passed to the AI ​​model as a prompt, and the generated answer is displayed to the user. The input data is the question, and the output is the answer from the generative AI model.

[0959] Through these steps, efficient management of business knowledge and rapid response in the field will be achieved.

[0960] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0961] MODE FOR CARRYING OUT THE INVENTION

[0962] This invention is a system for automatically storing and effectively utilizing business knowledge. This system combines a generative AI model and an emotion engine to review, refine, categorize, and search business knowledge documents, and provides information based on user emotions.

[0963] Document upload and parsing

[0964] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[0965] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[0966] Knowledge peer review and improvement

[0967] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[0968] Information structuring and automatic categorization

[0969] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[0970] Knowledge sharing and discovery

[0971] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[0972] Chat-style support

[0973] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[0974] Use of emotion engine

[0975] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[0976] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[0977] The server can also store the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements, allowing the system to provide more appropriate information according to the user's emotional state.

[0978] In this way, the present invention utilizes generative AI models and emotion engines to enable efficient storage, sharing, and search of business knowledge, as well as optimization of user experience.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] A user uploads a business knowledge document to the system using a terminal. For example, the user opens a browser, accesses the system's upload page, clicks the "Select File" button, selects the document file, and clicks the "Upload" button.

[0982] Step 2:

[0983] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[0984] Step 3:

[0985] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[0986] Step 4:

[0987] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[0988] Step 5:

[0989] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[0990] Step 6:

[0991] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[0992] Step 7:

[0993] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[0994] Step 8:

[0995] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[0996] Step 9:

[0997] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[0998] Step 10:

[0999] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[1000] Step 11:

[1001] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[1002] Step 12:

[1003] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[1004] Step 13:

[1005] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface, the tone of their search queries, etc.

[1006] Step 14:

[1007] The emotion engine analyzes the user's emotional state and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[1008] Step 15:

[1009] The server accumulates the emotional data obtained from the emotion engine as feedback and reflects it in future system improvements, enabling the system to provide more appropriate information according to the user's emotional state.

[1010] Through these steps, the system enables efficient storage, sharing, and search of business knowledge, as well as the provision of information based on user emotions.

[1011] Example 2

[1012] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1013] In conventional business knowledge management systems, storing and searching knowledge documents is done manually, requiring a great deal of time and effort. Furthermore, detecting inconsistencies and unclear expressions and proposing improvements is manual, making efficient improvements difficult. Furthermore, information is not provided based on the user's emotional state, resulting in low user convenience and satisfaction. There was a need to provide an efficient and flexible business knowledge management system that could solve these issues.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1015] In this invention, the server includes: means for uploading business knowledge data; means having a generative AI model for analyzing the uploaded data; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed data and saving it in a database; means for providing related information from the saved data based on a user's search query; means for instantly responding to user questions in chat format; means for passing real-time input data from the user to an emotion engine and analyzing the emotional state; and means for adapting system operation based on the analysis results of the emotion engine and accumulating feedback. This enables efficient storage, analysis, improvement, and sharing of business knowledge data, and optimal information provision based on the user's emotional state.

[1016] "Business knowledge data" is electronic data that records knowledge and information related to business.

[1017] "Uploading means" is a function that allows a user to send data from their own terminal to the server.

[1018] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and suggest improvements.

[1019] "Means for detecting inconsistencies and ambiguities" refers to the ability to use generative AI models to find errors or ambiguities in the data.

[1020] The "means for generating specific improvement proposals" is a function that automatically suggests appropriate correction methods for detected inconsistencies or unclear expressions.

[1021] "Means for automatically categorizing analyzed data and storing it in a database" refers to a function that classifies analyzed data based on its meaning, tags it appropriately, and stores it in a database.

[1022] "Means for providing relevant information from stored data based on a user's search query" refers to a function that extracts and provides appropriate information from a database based on search keywords entered by the user.

[1023] The "means for immediate response in chat format" is a function that responds in real time to questions entered by the user through a chat interface.

[1024] An "emotion engine" is an algorithm that analyzes a user's text input and behavioral data to understand the user's emotional state.

[1025] The "means for passing user input data to the emotion engine and analyzing the emotional state" is a function for sending user input information to the emotion engine and analyzing the user's emotions based on the results.

[1026] "Means for adapting the system's behavior based on the analysis results of the emotion engine" is a function that adjusts the system's information provision method and response content based on the user's emotional state.

[1027] "Means for accumulating feedback" is a function that records the analysis results of the emotion engine and user reactions in the system and uses them for future improvements.

[1028] This invention is a system that combines a generative AI model and an emotion engine to efficiently manage, analyze, improve, share, and search business knowledge data, and provide information based on user emotions as appropriate. A specific embodiment of this system will be described below.

[1029] System configuration

[1030] The system includes the following major hardware and software components:

[1031] 1. Server: The central hardware for data processing and storage.

[1032] 2. Terminal: A device (such as a personal computer or smartphone) that provides an interface for users to access the system.

[1033] 3. Generative AI model: A software algorithm that analyzes business knowledge data and generates specific improvement proposals.

[1034] 4. Emotion engine: An algorithm that analyzes user input data and adjusts the system's behavior based on the user's emotional state.

[1035] Program processing

[1036] Document upload and parsing

[1037] Users use their terminals to upload their own business knowledge data to the system. For example, a user selects a business report (Excel file) on their computer and uploads the file through the system's upload interface. At this stage, the data is sent to the server and saved.

[1038] The server passes the received data to a generative AI model for analysis. The generative AI model scans the document content to detect inconsistencies and unclear expressions. The specific software used is an analysis tool that uses natural language processing (NLP).

[1039] Knowledge peer review and improvement

[1040] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the data. For example, it detects incorrect data input or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the data as new entries.

[1041] Information structuring and automatic categorization

[1042] The server automatically categorizes the data analyzed by the generative AI model. For example, if the data content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores the data in a database as structured data for future searches.

[1043] Knowledge sharing and discovery

[1044] Users use their devices to access the system's search interface and search for the information they need. For example, they enter a search query like "marketing strategy for a new product." The server quickly extracts relevant data from its stock database and presents it to the user, allowing them to smoothly access the information they need.

[1045] Chat-style support

[1046] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[1047] Use of emotion engine

[1048] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[1049] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more friendly and detailed explanation. The server can also accumulate the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements.

[1050] Examples and prompts

[1051] For example:

[1052] 1. Document upload: "A user uploads an Excel file of a sales report from their computer to the system."

[1053] 2. Information search: "The user types 'tell me about the latest technology trends' into the search box, and the server provides the latest relevant technology reports."

[1054] 3. Chat support: "Users can type 'how to set up cloud storage' into the chat window, and the system will prompt them with the setup instructions."

[1055] Example prompts to input to a generative AI model:

[1056] Please analyze the sales report.

[1057] Tell us about the latest technology trends.

[1058] How do I set up cloud storage?

[1059] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1060] The flow of this system's program processing

[1061] Step 1:

[1062] Users use their terminals to upload business knowledge data to the system. For example, users select a business report (Excel file) on their terminal and upload the file through the system's upload interface. The input is the user's business knowledge file, and the output is that the file is sent to the server and saved.

[1063] Step 2:

[1064] The server passes the uploaded data to a generative AI model, which analyzes the content of the data and detects inconsistencies and unclear expressions. For example, the server passes the data through an NLP (natural language processing) tool to perform word frequency and context analysis. The input is business knowledge data, and the output is a list of inconsistencies and unclear expressions as a result of the analysis.

[1065] Step 3:

[1066] The server automatically generates specific improvement proposals based on the analysis results received from the generative AI model. For example, the generative AI model has the function of presenting "inconsistent data" and "correct data examples." The input is the analysis results of the generative AI model, and the output is new knowledge data with improvement proposals added.

[1067] Step 4:

[1068] The server automatically categorizes the analyzed and improved data and stores it in a database. For example, it can be classified into categories such as "project management" or "marketing." The input is knowledge data with improvement suggestions added, and the output is categorized and stored in the database as structured data.

[1069] Step 5:

[1070] A user uses a terminal to access the system's search interface and search for the information they need. For example, they enter a keyword such as "marketing strategy for a new product." The input is the user's search query, and the output is the relevant information in the database.

[1071] Step 6:

[1072] The server extracts relevant information from the database based on the search query and provides it to the user. For example, the server filters the appropriate data and displays the top results. The input is the user's search query, and the output is the filtered relevant information.

[1073] Step 7:

[1074] The user uses the chat interface of the system to input a question in real time, for example, "How do I create a budget plan for next year?" The input is the user's real-time question, and the output is the question being sent to the server.

[1075] Step 8:

[1076] The server uses a generative AI model to analyze the user's question and generate the optimal answer. For example, the generative AI model refers to past data to suggest an appropriate answer. The input is the user's question data, and the output is the generated answer.

[1077] Step 9:

[1078] The server instantly displays the generated response in the chat window for the user. At this time, the emotion engine analyzes the user's text input and determines their emotional state. The input is the response from the generative AI model and the user's text input, and the output is an optimized response and feedback of the emotional state.

[1079] Step 10:

[1080] Based on the emotion analysis results from the emotion engine, the server adjusts the system's behavior. For example, if the user is feeling frustrated, the system will provide a detailed and helpful explanation. The input is the emotion engine's analysis results, and the output is a tailored response or information delivery method.

[1081] Step 11:

[1082] The server accumulates the emotion data obtained from the emotion engine as feedback and reflects it in future system improvements. The input is emotion analysis feedback data, and the output is future system optimization.

[1083] Through these steps, the system enables efficient uploading, analysis, improvement, sharing, and searching of business knowledge data, as well as flexible information provision based on user sentiment.

[1084] (Application example 2)

[1085] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1086] Current factory robot management systems struggle to provide appropriate information in real time during maintenance and troubleshooting. Furthermore, they are unable to properly analyze the stress and frustration felt by users and provide appropriate support, resulting in a decline in user efficiency and satisfaction. In particular, there is a need to effectively utilize the large volume of business knowledge documents and quickly search and provide relevant information.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1088] In this invention, the server includes means for uploading business knowledge documents, means having a generative AI model for analyzing the uploaded documents, means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals, means for automatically categorizing the analyzed documents and storing them in a database, means for providing related information from the stored documents based on a user's search query, means for instantly responding to user questions in a chat format, and means for analyzing user emotions and adapting a service provision method based on the user's emotions. This enables effective management and search of business knowledge documents, provision of real-time support, and optimal responses according to user emotions.

[1089] "Business knowledge documents" refer to documents that contain business-related knowledge, procedures, data, etc.

[1090] "Upload" refers to a user sending a file from a local environment to a server.

[1091] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and performs text generation and analysis for specific tasks.

[1092] "Inconsistencies and unclear expressions" refer to contradictions in information within a document or expressions whose meaning is unclear.

[1093] "Specific improvement proposals" refer to proposals that show specific methods and content for correcting inconsistencies or unclear parts.

[1094] "Categorization" refers to classifying analyzed documents based on their content or characteristics.

[1095] A "database" refers to a system that allows information to be systematically stored, managed, and searched.

[1096] A "search query" refers to text that a user enters to search for specific information.

[1097] "Chat style" refers to an interface that provides information in an interactive format using text or voice.

[1098] "Immediate response" refers to replying to inquiries from users in real time.

[1099] "Emotion analysis" refers to analyzing a user's emotional state from text input or voice data.

[1100] "Adapting the service delivery method based on the user's emotions" refers to providing appropriate information and responses according to the user's emotional state based on the results of emotion analysis.

[1101] As an embodiment of the present invention, a system specialized for managing and maintaining factory robots will be developed. Each element of the system and its operation will be specifically described below.

[1102] System configuration

[1103] The system uses the following hardware and software:

[1104] Server: Parses, stores, and searches content (e.g., AWS EC2)

[1105] Devices: Smartphones and tablets are used (interfaces for administrators and staff)

[1106] Robots: Robots operating in factories (e.g. ABB, FANUC)

[1107] Generative AI models: Algorithms that analyze content, improve it, and generate responses (e.g., GPT-3)

[1108] Emotion engine: An engine for analyzing user emotions (e.g., EmotionAnalyzer)

[1109] Database: A system for storing and managing business knowledge documents (e.g., MySQL)

[1110] System Operation

[1111] 1. Uploading and analyzing business knowledge documents

[1112] Users upload business knowledge documents to the system using devices such as smartphones and tablets. The uploaded documents are sent to a server and analyzed by a generative AI model. This analysis detects inconsistencies and unclear expressions.

[1113] 2. Peer review and improvement of knowledge

[1114] The server receives the results of the document analysis using the generative AI model and generates specific improvement proposals. For example, it detects incorrect data entry or unclear explanations and creates improvement proposals to correct them. The improvement proposals are reflected in the document as new entries and are then saved again.

[1115] 3. Information structuring and automatic categorization

[1116] The server automatically categorizes documents analyzed by the generative AI model, classifying document content into specific categories and adding relevant tags, storing structured data in a database for future searches.

[1117] 4. Knowledge sharing and discovery

[1118] Users can use their terminals to access the system's search interface and search for the information they need, for example, by searching for "robot maintenance procedures." The server then extracts relevant business knowledge documents from the database and provides them to the user.

[1119] 5. Chat support

[1120] Users can use the system's chat interface to ask questions in real time. For example, "What should I do if my machine is behaving abnormally?" The server uses a generative AI model to analyze the question and generate the optimal answer. The generated answer is immediately displayed in the chat window, allowing users to quickly obtain the information they need.

[1121] 6. Use of Emotion Engines

[1122] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will recognize this and adjust the system to provide a kind response.

[1123] Specific examples

[1124] A factory manager can upload a machine maintenance manual using a smartphone. The system detects any unclear areas and uses a generative AI model to improve them. For example, the system might prompt the user to "scan this document and improve the unclear sections." The improvements and categories are then saved in a database, allowing for quicker response if a similar issue arises in the future.

[1125] In addition, when an administrator searches for "robot maintenance procedures" in the search interface, the system provides relevant information. When an administrator asks "What should I do if my machine is behaving abnormally?" in the chat interface, the system provides the most appropriate answer. By also using an emotion engine, it becomes possible to provide information that takes into account the user's emotions.

[1126] In this way, a system is realized that allows for efficient and effective management and maintenance of the factory.

[1127] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1128] Step 1:

[1129] A user uploads a business knowledge document using a terminal. Specifically, the user selects a file from a smartphone or tablet and presses the upload button, which sends the document to the server. The input at this time is the document file selected by the user, and the output is the document data saved on the server.

[1130] Step 2:

[1131] The server passes the uploaded document to a generative AI model that analyzes it. The generative AI model scans the document's contents to detect inconsistencies and ambiguous expressions. The input is the document data stored on the server, and the output is the analysis results (a list of inconsistencies and ambiguous expressions).

[1132] Step 3:

[1133] The server generates specific improvement proposals based on the analysis results obtained by the generative AI model. Based on the analysis results, the AI ​​model generates specific methods and content for correcting inconsistencies and unclear parts of the document. The input is the analysis results, and the output is a document that reflects the improvement proposals.

[1134] Step 4:

[1135] The server automatically categorizes the improved documents and generates relevant tags. The generated documents are classified into specific categories and have tags added to them for future searches. The input is the document with the proposed improvements, and the output is structured data with categories and tags.

[1136] Step 5:

[1137] The server stores the structured data with categories and tags in a database, which can then be quickly served to users for future searches. The input is documents with categories and tags, and the output is the document data stored in the database.

[1138] Step 6:

[1139] A user accesses the search interface using a terminal and searches for the information they need. The server extracts and provides relevant documents from the database based on the user's search query. The input is the user's search query, and the output is the relevant documents provided as search results.

[1140] Step 7:

[1141] Users input questions in real time using the system's chat interface. The server analyzes the user's question using a generative AI model and generates an optimal answer. The input is the user's question, and the output is the answer generated by the generative AI model.

[1142] Step 8:

[1143] The server passes the user's input text or voice data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the user's emotional state and adapts the service delivery method based on the results. The input is the user's text or voice data, and the output is the emotion analysis results and a response method based on that.

[1144] For example, when an administrator inputs the prompt "Scan this document and improve the unclear sections," the generative AI model identifies the unclear areas and generates specific improvement suggestions. The improvements are saved in the database with categories and tags, and are quickly made available to users in response to searches or inquiries.

[1145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1147] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1148] [Fourth embodiment]

[1149] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1150] 7, a 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.

[1151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1153] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1158] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1160] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1161] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1162] MODE FOR CARRYING OUT THE INVENTION

[1163] This invention is a system for automatically storing and effectively utilizing business knowledge. This system uses a generative AI model to review, improve, and appropriately categorize business knowledge documents, allowing users to smoothly obtain the information they need.

[1164] Document upload and parsing

[1165] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[1166] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[1167] Knowledge peer review and improvement

[1168] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[1169] Information structuring and automatic categorization

[1170] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[1171] Knowledge sharing and discovery

[1172] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[1173] Chat-style support

[1174] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[1175] The above is an embodiment of the present invention. This system makes it possible to efficiently store and share business knowledge and provide an environment in which users can quickly and accurately obtain the information they need.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] A user uploads a business knowledge document to the system using a terminal. The user opens a browser and accesses the system's upload page. Then, the user clicks the "Select File" button to select the document file and clicks the "Upload" button.

[1179] Step 2:

[1180] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[1181] Step 3:

[1182] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[1183] Step 4:

[1184] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[1185] Step 5:

[1186] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[1187] Step 6:

[1188] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[1189] Step 7:

[1190] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[1191] Step 8:

[1192] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[1193] Step 9:

[1194] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[1195] Step 10:

[1196] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[1197] Step 11:

[1198] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[1199] Step 12:

[1200] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[1201] Through these steps, the system enables efficient storage and sharing of business knowledge, providing an environment in which users can quickly and accurately obtain the information they need.

[1202] Example 1

[1203] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1204] The vast volume and complexity of business knowledge data makes it difficult to efficiently organize it and quickly obtain the necessary information. Furthermore, the manual correction of data containing inconsistencies or unclear expressions takes time and effort, so there is a need to improve efficiency.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1206] In this invention, the server includes: means for a user to upload business knowledge data using an information processing device; means for receiving the uploaded data and passing it to a generative AI model as an analysis target; means for analyzing the data using the generative AI model to detect inconsistencies and unclear expressions and generate specific improvement proposals; means for automatically categorizing the analyzed data, adding associated tags, and saving the data as structured data in a database; means for quickly providing related information from the saved data based on a user's search query; and means for generating answers to user questions in real time using the generative AI model and providing immediate responses in chat format. This allows business knowledge data to be organized efficiently, making it possible to quickly and accurately obtain necessary information.

[1207] A "user" is an individual or organization that uses the system to upload and search business knowledge data.

[1208] An "information processing device" is a device that a user uses to upload business knowledge data, and includes a personal computer, a tablet, a smartphone, and the like.

[1209] "Business knowledge data" refers to documents, files, or data sets that contain business-related knowledge or information.

[1210] The "upload interface" is an online interface that allows users to send business knowledge data to the server.

[1211] A "generative AI model" is an artificial intelligence model that analyzes received data, detects inconsistencies and unclear expressions, and generates specific improvement proposals.

[1212] The "server" is a central computer system that receives, stores, and analyzes uploaded business knowledge data, drives generative AI models, and executes various processes.

[1213] "Analysis target" refers to the data that will be analyzed by the generative AI model after being uploaded to the server.

[1214] An "inconsistency" is an area in the data where there is a lack of consistency or contradiction.

[1215] "Unclear expression" means an expression in which the data content is vague and difficult to understand.

[1216] "Specific improvement suggestions" are correction suggestions automatically generated by the generative AI model to resolve inconsistencies or unclear expressions.

[1217] "Categorization" is the process of classifying analyzed data into specific categories.

[1218] "Tags" are additional information that add highly relevant keywords to data to make it easier to search for later.

[1219] A "database" is a digital storage device for storing structured data that has been analyzed and archived.

[1220] A "search query" is a keyword or phrase that a user enters to search for desired information.

[1221] "Chat format" means that users input questions in real time and the system responds immediately.

[1222] "Generating answers in real time" means automatically and quickly generating answers immediately after a user enters a question.

[1223] MODE FOR CARRYING OUT THE INVENTION

[1224] This invention is a system for efficiently managing business knowledge data and enabling users to quickly and accurately obtain the information they need. This system uses a generative AI model to analyze business knowledge data and automatically detect and correct data inconsistencies and unclear expressions. It also automatically categorizes the analyzed data, adds tags, and stores it in a database as structured data, providing an environment that makes it easy for users to search.

[1225] Uploading and saving documents

[1226] A user uploads business knowledge data using an information processing device (e.g., a PC or tablet). The user selects a file through the system's upload interface and sends it to the system. For example, if a user uploads an Excel file (e.g., excel_file.xlsx), the file is sent to the server and stored in the database along with metadata (upload date and time, file size, etc.).

[1227] Document Parsing

[1228] The server passes the stored document to a generative AI model for analysis. The generative AI model scans the document for inconsistencies and unclear expressions. For example, it reads the contents of each cell in an Excel spreadsheet and checks for inconsistencies.

[1229] Knowledge peer review and improvement

[1230] The server receives the analysis results of the generative AI model and automatically generates specific improvement proposals for detected inconsistencies and unclear expressions. For example, to a cell that simply says "Sales forecast," add a detailed explanation such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry.

[1231] Information structuring and automatic categorization

[1232] The server automatically categorizes the analyzed documents using the generative AI model. For example, a document related to "project management" would be classified under the "project management" category. Additionally, related tags such as "budget planning" and "marketing strategy" are automatically added as needed and saved in the database.

[1233] Knowledge sharing and discovery

[1234] A user accesses the system's search interface using a terminal and enters a query, for example, "marketing strategy for a new product," into the search box. The server then quickly retrieves relevant documents from the database and displays them to the user. The retrieved documents are presented in a list format in order of relevance.

[1235] Chat-style support

[1236] A user can use the system's chat interface to input a question. For example, "How do I create a budget plan for next year?" The server analyzes this question using a generative AI model and automatically generates an appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to review its contents and obtain the necessary information.

[1237] Specific operation example

[1238] Upload: The user uploads an Excel file (excel_file.xlsx) from their computer. The server receives and saves this file.

[1239] Analysis: The server passes the file to the generative AI model for analysis. For example, if cell B4 contains only "Sales Forecast," the generative AI model will detect the inconsistency.

[1240] Improvement: The server adds a specific improvement proposal: "Sales forecast (Q1 FY2023) - Estimated amount: 5 million yen."

[1241] Categorize: The server categorizes the file into the "Project Management" category and adds the "Budget Planning" tag.

[1242] Search: When a user searches for "marketing strategy for a new product," relevant documents are displayed.

[1243] Chat support: When a user types, "How do I create a budget plan for next year?", the server instantly generates and displays the appropriate answer.

[1244] Prompt Sentence Examples

[1245] Below are some examples of prompt sentences.

[1246] Document Upload: "Upload your business knowledge data. The system will analyze the data and detect inconsistencies or unclear wording."

[1247] Improved analysis results: "The generative AI model detected the following unclear areas and provided specific suggestions for improvement."

[1248] Enter a search query: "Enter 'marketing strategy for a new product' in the system's search interface. Relevant documents will be displayed."

[1249] Chat support example: "Type 'How do I create a budget plan for next year?' into the chat interface."

[1250] The above is an embodiment of the present invention. By using this system, business knowledge data can be efficiently organized, and necessary information can be obtained quickly and accurately.

[1251] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1252] Step 1:

[1253] A user uploads business knowledge data (e.g., excel_file.xlsx) using an information processing device. The user clicks the "Select File" button in the system's upload interface, selects a file, and presses the upload button. The input here is the business knowledge data, and the output is the data sent to the server.

[1254] Step 2:

[1255] The server receives the uploaded data and stores it in a database along with metadata (upload date and time, file size, etc.). At this time, the server prepares the received data to be passed to the generative AI model for analysis. The input is the uploaded business knowledge data, and the output is the stored data.

[1256] Step 3:

[1257] The server passes the stored data to the generative AI model, which then begins analysis. The generative AI model scans the data to detect inconsistencies and unclear expressions. In this case, the input is business knowledge data, and the output is the analysis results. For example, each cell in an Excel spreadsheet is read one by one, and the contents are checked for inconsistencies.

[1258] Step 4:

[1259] The server automatically generates specific improvement proposals based on the analysis results obtained from the generative AI model. For example, for a cell that simply says "Sales forecast," it generates a detailed improvement proposal such as "Sales forecast (FY2023 Q1) - Estimated amount: 5 million yen." The improvement proposal is added to the original document as a new entry. The input here is the analysis result from the generative AI model, and the output is a new document that reflects the improvement proposal.

[1260] Step 5:

[1261] The server automatically categorizes the data analyzed and improved by the generative AI model. For example, data related to "project management" is classified into the "project management" category and related tags such as "budget planning" and "marketing strategy" are automatically added. The input is a document with the proposed improvements reflected, and the output is categorized and tagged structured data.

[1262] Step 6:

[1263] The server stores the categorized data in a database. The data is stored as structured data with category and tag information, making it easier to search later. The input is data with categories and tags, and the output is the data stored in the database.

[1264] Step 7:

[1265] A user accesses the system's search interface using a terminal and enters a search query, for example, "marketing strategy for a new product" in the search box. The server retrieves relevant information from the database based on the entered query and displays it to the user. Here, the input is the search query, and the output is a list of relevant documents.

[1266] Step 8:

[1267] A user enters a question into the system's chat interface. For example, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate an appropriate answer. The generated answer is immediately displayed in the chat window. The input here is the user's question, and the output is the answer from the generative AI model.

[1268] The above are the specific processing steps of this system. The specific operations performed at each step are necessary to achieve efficient management of business knowledge data and rapid information acquisition.

[1269] (Application example 1)

[1270] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1271] Conventional business knowledge management systems have problems with inefficient document uploading, analysis, improvement, categorization, search, and real-time response. Especially in large-scale business environments such as logistics centers, manual business knowledge management is time-consuming, labor-intensive, and inefficient. Furthermore, there is no established method for utilizing general-purpose computing devices such as smartphones, making it difficult to respond immediately on-site.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1273] In this invention, the server includes: means for uploading business knowledge documents; means for having a generative AI model that analyzes the uploaded documents; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed documents and saving them in a database; means for providing related information from the saved documents based on a user's search query; means for instantly responding to user questions in chat format; means for scanning and uploading business knowledge documents via a smartphone or general-purpose computing device; and means for classifying the business knowledge documents analyzed by the generative AI model into categories such as project management, quality control, and inventory management. This enables efficient management of business knowledge, rapid on-site response, and appropriate categorization.

[1274] A "business knowledge document" is a document that contains knowledge and information about business in a company or organization.

[1275] "Document upload" is an operation in which a user sends their own electronic files or scanned paper documents to the system.

[1276] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate improvement suggestions based on the analysis results.

[1277] "Inconsistencies and unclear expressions" refers to conflicting information or parts of a document that are difficult to understand.

[1278] "Specific improvement proposals" are proposals based on the analysis results to make the content of the document clearer and more accurate.

[1279] "Automatic categorization" is the process of classifying analyzed documents into appropriate categories using a generative AI model.

[1280] A "database" is a data storage system that structures and stores parsed documents and other related information.

[1281] A "search query" is a keyword or phrase that a user enters into a system to retrieve desired information.

[1282] "Chat-style instant response" is an interface that provides answers to questions from users in real time.

[1283] A "smartphone or general-purpose computing device" is a portable electronic device or general-purpose computer used to perform operations such as scanning, uploading, and searching.

[1284] "Generative AI categorization" is the process by which a generative AI model analyzes the content of a document and appropriately classifies it into categories such as project management, quality control, or inventory management.

[1285] The system described in this invention, "LogiMaster," efficiently manages business knowledge documents and is intended for use in logistics centers in particular. This system is realized using the following hardware and software.

[1286] Hardware used

[1287] Server: A central computing device that stores, analyzes, and processes documents.

[1288] Smartphone or general-purpose computing device: A handheld device or general-purpose computer for scanning documents, uploading documents, entering search queries, and chatting.

[1289] Software used

[1290] Flask: Used as a web framework to provide the user interface and API endpoints.

[1291] OpenAI API: Responsible for analyzing documents with generative AI models and generating improvement suggestions.

[1292] SQLAlchemy: An ORM library for database management that uses SQLite as a backend.

[1293] Data processing and calculation

[1294] The server receives business knowledge documents uploaded by users and analyzes and proposes improvements through a generative AI model. Documents uploaded using smartphones or general-purpose computing devices are processed as follows:

[1295] Document upload: Users can use their smartphone camera to scan paper documents or directly upload existing electronic files.

[1296] Analysis and improvement suggestions: Documents sent to the server are analyzed by a generative AI model using the OpenAI API to detect inconsistencies and unclear expressions and suggest specific improvement suggestions.

[1297] Categorization and database storage: Parsed documents are automatically categorized into categories such as project management, quality control, inventory management, etc. and stored in a database using SQLAlchemy.

[1298] Search and chat functionality: When a user enters a search query, the server extracts and provides relevant information from stored documents, and a generative AI model responds immediately to the user's questions using a real-time chat interface.

[1299] Specific examples

[1300] For example, if a logistics center manager wants to search for "new ways to optimize inventory procedures," he or she can enter the following query on a smartphone:

[1301] How can you optimize your new receiving procedures?

[1302] Based on this query, the server uses a generative AI model to search for relevant business knowledge documents and provide the most appropriate information.

[1303] A specific example of a prompt sentence for a generative AI model is as follows:

[1304] Please analyze the following business knowledge document and suggest improvements:

[1305] ---

[1306] Document Title: "Logistics Center Financial Report"

[1307] 1. Consider ways to reduce inventory costs

[1308] 2. Improving work efficiency

[1309] 3. Automation of work through mechanization

[1310] 4. ...

[1311] Please analyze and suggest improvements.

[1312] In this way, the system based on the present invention efficiently manages business knowledge and provides support for quickly obtaining required information.

[1313] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1314] Step 1:

[1315] A user uploads a business knowledge document using a smartphone or general-purpose computing device. Specifically, the user scans a paper document with the smartphone camera or selects an existing electronic file and presses the upload button. The input data is the document file, and the output is the data sent to the server.

[1316] Step 2:

[1317] The server saves the uploaded document. Specifically, it receives the document file using Flask and saves it in a specific folder on the server. The input data is the data sent by the user, and the output is a file saved on the server.

[1318] Step 3:

[1319] The server passes the saved document to the generative AI model for analysis. Specifically, it uses the OpenAI API to send the document content to the analysis model in the form of a prompt. The input data is the saved document content, and the output is the analysis result of the generative AI model.

[1320] Step 4:

[1321] The generative AI model detects inconsistencies and unclear expressions in the document and proposes specific improvement suggestions. Specifically, the generative AI model extracts improvement suggestions from the analysis results it provides and adds them to the document as new comments or corrections. The input data are the analysis results, and the output is a new document containing the improvement suggestions.

[1322] Step 5:

[1323] The server automatically categorizes documents analyzed by the generative AI model, specifically assigning categories such as project management, quality control, and inventory management based on the document content. The input data is the refined document content, and the output is the classified category.

[1324] Step 6:

[1325] The server stores the categorized documents in a database. Specifically, it uses SQLAlchemy to store the document content and category information in the database. The input data is the document with classification information, and the output is the saved state of the database.

[1326] Step 7:

[1327] A user inputs a search query using a smartphone or a general-purpose computing device. Specifically, the user inputs keywords into a search interface and presses a search button. The input data is the search query, and the output is a request for search results.

[1328] Step 8:

[1329] The server searches for and provides relevant documents in the database based on the search query. Specifically, it uses SQLAlchemy to extract documents that match the query and display them to the user. The input data is the user's search query, and the output is a list of relevant documents.

[1330] Step 9:

[1331] A user inputs a question in real time using the chat interface. Specifically, the user inputs a question in the chat window and presses the send button. The input data is a chat-style question, and the output is a request for an immediate response.

[1332] Step 10:

[1333] The server uses a generative AI model to respond to the user's questions immediately. Specifically, the input question is passed to the AI ​​model as a prompt, and the generated answer is displayed to the user. The input data is the question, and the output is the answer from the generative AI model.

[1334] Through these steps, efficient management of business knowledge and rapid response in the field will be achieved.

[1335] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1336] MODE FOR CARRYING OUT THE INVENTION

[1337] This invention is a system for automatically storing and effectively utilizing business knowledge. This system combines a generative AI model and an emotion engine to review, refine, categorize, and search business knowledge documents, and provides information based on user emotions.

[1338] Document upload and parsing

[1339] The user uploads their own business knowledge document using a terminal. For example, the user selects an Excel file on their computer and uploads the file through the system's upload interface. At this stage, the document is sent to the server and saved.

[1340] The server passes the received document to the generative AI model for analysis, which scans the document's contents to detect inconsistencies and unclear expressions.

[1341] Knowledge peer review and improvement

[1342] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the document. For example, it detects incorrect data entry or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the document as new entries.

[1343] Information structuring and automatic categorization

[1344] The server automatically categorizes documents analyzed by the generative AI model. For example, if a document's content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores them in a database as structured data for future searches.

[1345] Knowledge sharing and discovery

[1346] A user accesses the system's search interface using a terminal and searches for the information they need. For example, they enter a search query such as "marketing strategy for a new product." The server quickly extracts relevant documents from the stock database and presents them to the user. This allows the user to smoothly access the information they need.

[1347] Chat-style support

[1348] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[1349] Use of emotion engine

[1350] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[1351] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[1352] The server can also store the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements, allowing the system to provide more appropriate information according to the user's emotional state.

[1353] In this way, the present invention utilizes a generative AI model and an emotion engine to enable efficient storage, sharing, and search of business knowledge, as well as optimization of the user experience.

[1354] The processing flow will be explained below.

[1355] Step 1:

[1356] A user uploads a business knowledge document to the system using a terminal. For example, the user opens a browser, accesses the system's upload page, clicks the "Select File" button, selects the document file, and clicks the "Upload" button.

[1357] Step 2:

[1358] The server receives the uploaded document, which is then stored in temporary storage and prepared for analysis.

[1359] Step 3:

[1360] The server transfers the document file to the generative AI model, which then reads the document's contents and detects inconsistencies and unclear expressions.

[1361] Step 4:

[1362] The server receives the analysis results of the generative AI model and lists inconsistencies and unclear expressions in the document, and the generative AI model generates specific improvement suggestions based on them.

[1363] Step 5:

[1364] The server applies the suggested improvements to the document and generates a new version of the document, which is improved where further correction or review is needed.

[1365] Step 6:

[1366] The server uses a generative AI model to analyze the content of the enhanced document and automatically categorize it. After determining the category, the document is assigned appropriate tags.

[1367] Step 7:

[1368] The server stores the categorized and tagged documents in a database as structured data, allowing for quick searches for needed information later.

[1369] Step 8:

[1370] A user accesses the system's search interface using a terminal and searches for the required information. The user enters a search query and clicks the "Search" button.

[1371] Step 9:

[1372] The server receives a user's search query and retrieves relevant documents from the database, which are then prioritized and served to the user.

[1373] Step 10:

[1374] Users can check the search results and download or view the relevant documents as needed, which allows for smooth utilization of business knowledge.

[1375] Step 11:

[1376] Users use a chat-style interface on their devices to type questions in real time, such as "How do I create a budget plan for next year?"

[1377] Step 12:

[1378] The server uses a generative AI model to analyze the user's question and generate the best answer, which is then instantly displayed in the chat window.

[1379] Step 13:

[1380] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface, the tone of their search queries, etc.

[1381] Step 14:

[1382] The emotion engine analyzes the user's emotional state and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more helpful and detailed explanation.

[1383] Step 15:

[1384] The server accumulates the emotional data obtained from the emotion engine as feedback and reflects it in future system improvements, enabling the system to provide more appropriate information according to the user's emotional state.

[1385] Through these steps, the system enables efficient storage, sharing, and search of business knowledge, as well as the provision of information based on user emotions.

[1386] Example 2

[1387] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1388] In conventional business knowledge management systems, storing and searching knowledge documents is done manually, requiring a great deal of time and effort. Furthermore, detecting inconsistencies and unclear expressions and proposing improvements is manual, making efficient improvements difficult. Furthermore, information is not provided based on the user's emotional state, resulting in low user convenience and satisfaction. There was a need to provide an efficient and flexible business knowledge management system that could solve these issues.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1390] In this invention, the server includes: means for uploading business knowledge data; means having a generative AI model for analyzing the uploaded data; means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals; means for automatically categorizing the analyzed data and saving it in a database; means for providing related information from the saved data based on a user's search query; means for instantly responding to user questions in chat format; means for passing real-time input data from the user to an emotion engine and analyzing the emotional state; and means for adapting system operation based on the analysis results of the emotion engine and accumulating feedback. This enables efficient storage, analysis, improvement, and sharing of business knowledge data, and optimal information provision based on the user's emotional state.

[1391] "Business knowledge data" is electronic data that records knowledge and information related to business.

[1392] "Uploading means" is a function that allows a user to send data from their own terminal to the server.

[1393] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically analyze data and suggest improvements.

[1394] "Means for detecting inconsistencies and ambiguities" refers to the ability to use generative AI models to find errors or ambiguities in the data.

[1395] The "means for generating specific improvement proposals" is a function that automatically suggests appropriate correction methods for detected inconsistencies or unclear expressions.

[1396] "Means for automatically categorizing analyzed data and storing it in a database" refers to a function that classifies analyzed data based on its meaning, tags it appropriately, and stores it in a database.

[1397] "Means for providing relevant information from stored data based on a user's search query" refers to a function that extracts and provides appropriate information from a database based on search keywords entered by the user.

[1398] The "means for immediate response in chat format" is a function that responds in real time to questions entered by the user through a chat interface.

[1399] An "emotion engine" is an algorithm that analyzes a user's text input and behavioral data to understand the user's emotional state.

[1400] The "means for passing user input data to the emotion engine and analyzing the emotional state" is a function for sending user input information to the emotion engine and analyzing the user's emotions based on the results.

[1401] "Means for adapting the system's behavior based on the analysis results of the emotion engine" is a function that adjusts the system's information provision method and response content based on the user's emotional state.

[1402] "Means for accumulating feedback" is a function that records the analysis results of the emotion engine and user reactions in the system and uses them for future improvements.

[1403] This invention is a system that combines a generative AI model and an emotion engine to efficiently manage, analyze, improve, share, and search business knowledge data, and provide information based on user emotions as appropriate. A specific embodiment of this system will be described below.

[1404] System configuration

[1405] The system includes the following major hardware and software components:

[1406] 1. Server: The central hardware for data processing and storage.

[1407] 2. Terminal: A device (such as a personal computer or smartphone) that provides an interface for users to access the system.

[1408] 3. Generative AI model: A software algorithm that analyzes business knowledge data and generates specific improvement proposals.

[1409] 4. Emotion engine: An algorithm that analyzes user input data and adjusts the system's behavior based on the user's emotional state.

[1410] Program processing

[1411] Document upload and parsing

[1412] Users use their terminals to upload their own business knowledge data to the system. For example, a user selects a business report (Excel file) on their computer and uploads the file through the system's upload interface. At this stage, the data is sent to the server and saved.

[1413] The server passes the received data to a generative AI model for analysis. The generative AI model scans the document content to detect inconsistencies and unclear expressions. The specific software used is an analysis tool that uses natural language processing (NLP).

[1414] Knowledge peer review and improvement

[1415] The server receives the analysis results of the generative AI model and automatically generates suggestions for improving the data. For example, it detects incorrect data input or unclear explanations and generates specific suggestions for improvement. The suggestions are added to the data as new entries.

[1416] Information structuring and automatic categorization

[1417] The server automatically categorizes the data analyzed by the generative AI model. For example, if the data content is related to project management, it will be classified into the appropriate category. It also adds relevant tags and stores the data in a database as structured data for future searches.

[1418] Knowledge sharing and discovery

[1419] Users use their devices to access the system's search interface and search for the information they need. For example, they enter a search query like "marketing strategy for a new product." The server quickly extracts relevant data from its stock database and presents it to the user, allowing them to smoothly access the information they need.

[1420] Chat-style support

[1421] When a user uses the system's chat interface, they input a question in real time, such as, "How do I create a budget plan for next year?" The server uses a generative AI model to analyze the question and generate the most appropriate answer. The generated answer is immediately displayed in the chat window, allowing the user to quickly obtain the information they need.

[1422] Use of emotion engine

[1423] The server passes real-time input data from users to the emotion engine, which analyzes their emotions. For example, it collects emotion data based on the text users send through the chat interface and the tone of their search queries.

[1424] The emotion engine analyzes the user's emotional state (e.g., stressed, excited, calm, etc.) and adapts the system's behavior based on the results. For example, if the user is feeling frustrated, the emotion engine will recognize this and adjust the system to provide a more friendly and detailed explanation. The server can also accumulate the emotional data obtained from the emotion engine as feedback and reflect it in future system improvements.

[1425] Examples and prompts

[1426] For example:

[1427] 1. Document upload: "A user uploads an Excel file of a sales report from their computer to the system."

[1428] 2. Information search: "The user types 'tell me about the latest technology trends' into the search box, and the server provides the latest relevant technology reports."

[1429] 3. Chat support: "Users can type 'how to set up cloud storage' into the chat window, and the system will prompt them with the setup instructions."

[1430] Example prompts to input to a generative AI model:

[1431] Please analyze the sales report.

[1432] Tell us about the latest technology trends.

[1433] How do I set up cloud storage?

[1434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1435] The flow of this system's program processing

[1436] Step 1:

[1437] Users use their terminals to upload business knowledge data to the system. For example, users select a business report (Excel file) on their terminal and upload the file through the system's upload interface. The input is the user's business knowledge file, and the output is that the file is sent to the server and saved.

[1438] Step 2:

[1439] The server passes the uploaded data to a generative AI model, which analyzes the content of the data and detects inconsistencies and unclear expressions. For example, the server passes the data through an NLP (natural language processing) tool to perform word frequency and context analysis. The input is business knowledge data, and the output is a list of inconsistencies and unclear expressions as a result of the analysis.

[1440] Step 3:

[1441] The server automatically generates specific improvement proposals based on the analysis results received from the generative AI model. For example, the generative AI model has the function of presenting "inconsistent data" and "correct data examples." The input is the analysis results of the generative AI model, and the output is new knowledge data with improvement proposals added.

[1442] Step 4:

[1443] The server automatically categorizes the analyzed and improved data and stores it in a database. For example, it can be classified into categories such as "project management" or "marketing." The input is knowledge data with improvement suggestions added, and the output is categorized and stored in the database as structured data.

[1444] Step 5:

[1445] A user uses a terminal to access the system's search interface and search for the information they need. For example, they enter a keyword such as "marketing strategy for a new product." The input is the user's search query, and the output is the relevant information in the database.

[1446] Step 6:

[1447] The server extracts relevant information from the database based on the search query and provides it to the user. For example, the server filters the appropriate data and displays the top results. The input is the user's search query, and the output is the filtered relevant information.

[1448] Step 7:

[1449] The user uses the chat interface of the system to input a question in real time, for example, "How do I create a budget plan for next year?" The input is the user's real-time question, and the output is the question being sent to the server.

[1450] Step 8:

[1451] The server uses a generative AI model to analyze the user's question and generate the optimal answer. For example, the generative AI model refers to past data to suggest an appropriate answer. The input is the user's question data, and the output is the generated answer.

[1452] Step 9:

[1453] The server instantly displays the generated response in the chat window for the user. At this time, the emotion engine analyzes the user's text input and determines their emotional state. The input is the response from the generative AI model and the user's text input, and the output is an optimized response and feedback of the emotional state.

[1454] Step 10:

[1455] Based on the emotion analysis results from the emotion engine, the server adjusts the system's behavior. For example, if the user is feeling frustrated, the system will provide a detailed and helpful explanation. The input is the emotion engine's analysis results, and the output is a tailored response or information delivery method.

[1456] Step 11:

[1457] The server accumulates the emotion data obtained from the emotion engine as feedback and reflects it in future system improvements. The input is emotion analysis feedback data, and the output is future system optimization.

[1458] Through these steps, the system enables efficient uploading, analysis, improvement, sharing, and searching of business knowledge data, as well as flexible information provision based on user sentiment.

[1459] (Application example 2)

[1460] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1461] Current factory robot management systems struggle to provide appropriate information in real time during maintenance and troubleshooting. Furthermore, they are unable to properly analyze the stress and frustration felt by users and provide appropriate support, resulting in a decline in user efficiency and satisfaction. In particular, there is a need to effectively utilize the large volume of business knowledge documents and quickly search and provide relevant information.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1463] In this invention, the server includes means for uploading business knowledge documents, means having a generative AI model for analyzing the uploaded documents, means for detecting inconsistencies and unclear expressions using the generative AI model and generating specific improvement proposals, means for automatically categorizing the analyzed documents and storing them in a database, means for providing related information from the stored documents based on a user's search query, means for instantly responding to user questions in a chat format, and means for analyzing user emotions and adapting a service provision method based on the user's emotions. This enables effective management and search of business knowledge documents, provision of real-time support, and optimal responses according to user emotions.

[1464] "Business knowledge documents" refer to documents that contain business-related knowledge, procedures, data, etc.

[1465] "Upload" refers to a user sending a file from a local environment to a server.

[1466] A "generative AI model" refers to an artificial intelligence algorithm that learns from large amounts of data and performs text generation and analysis for specific tasks.

[1467] "Inconsistencies and unclear expressions" refer to contradictions in information within a document or expressions whose meaning is unclear.

[1468] "Specific improvement proposals" refer to proposals that show specific methods and content for correcting inconsistencies or unclear parts.

[1469] "Categorization" refers to classifying analyzed documents based on their content or characteristics.

[1470] A "database" refers to a system that allows information to be systematically stored, managed, and searched.

[1471] A "search query" refers to text that a user enters to search for specific information.

[1472] "Chat style" refers to an interface that provides information in an interactive format using text or voice.

[1473] "Immediate response" refers to replying to inquiries from users in real time.

[1474] "Emotion analysis" refers to analyzing a user's emotional state from text input or voice data.

[1475] "Adapting the service delivery method based on the user's emotions" refers to providing appropriate information and responses according to the user's emotional state based on the results of emotion analysis.

[1476] As an embodiment of the present invention, a system specialized for managing and maintaining factory robots will be developed. Each element of the system and its operation will be specifically described below.

[1477] System configuration

[1478] The system uses the following hardware and software:

[1479] Server: Parses, stores, and searches content (e.g., AWS EC2)

[1480] Devices: Smartphones and tablets are used (interfaces for administrators and staff)

[1481] Robots: Robots operating in factories (e.g. ABB, FANUC)

[1482] Generative AI models: Algorithms that analyze content, improve it, and generate responses (e.g., GPT-3)

[1483] Emotion engine: An engine for analyzing user emotions (e.g., EmotionAnalyzer)

[1484] Database: A system for storing and managing business knowledge documents (e.g., MySQL)

[1485] System Operation

[1486] 1. Uploading and analyzing business knowledge documents

[1487] Users upload business knowledge documents to the system using devices such as smartphones and tablets. The uploaded documents are sent to a server and analyzed by a generative AI model. This analysis detects inconsistencies and unclear expressions.

[1488] 2. Peer review and improvement of knowledge

[1489] The server receives the results of the document analysis using the generative AI model and generates specific improvement proposals. For example, it detects incorrect data entry or unclear explanations and creates improvement proposals to correct them. The improvement proposals are reflected in the document as new entries and are then saved again.

[1490] 3. Information structuring and automatic categorization

[1491] The server automatically categorizes documents analyzed by the generative AI model, classifying document content into specific categories and adding relevant tags, storing structured data in a database for future searches.

[1492] 4. Knowledge sharing and discovery

[1493] Users can use their terminals to access the system's search interface and search for the information they need, for example, by searching for "robot maintenance procedures." The server then extracts relevant business knowledge documents from the database and provides them to the user.

[1494] 5. Chat support

[1495] Users can use the system's chat interface to ask questions in real time. For example, "What should I do if my machine is behaving abnormally?" The server uses a generative AI model to analyze the question and generate the optimal answer. The generated answer is immediately displayed in the chat window, allowing users to quickly obtain the information they need.

[1496] 6. Use of Emotion Engines

[1497] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will recognize this and adjust the system to provide a kind response.

[1498] Specific examples

[1499] A factory manager can upload a machine maintenance manual using a smartphone. The system detects any unclear areas and uses a generative AI model to improve them. For example, the system might prompt the user to "scan this document and improve the unclear sections." The improvements and categories are then saved in a database, allowing for quicker response if a similar issue arises in the future.

[1500] In addition, when an administrator searches for "robot maintenance procedures" in the search interface, the system provides relevant information. When an administrator asks "What should I do if my machine is behaving abnormally?" in the chat interface, the system provides the most appropriate answer. By also using an emotion engine, it becomes possible to provide information that takes into account the user's emotions.

[1501] In this way, a system is realized that allows for efficient and effective management and maintenance of the factory.

[1502] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1503] Step 1:

[1504] A user uploads a business knowledge document using a terminal. Specifically, the user selects a file from a smartphone or tablet and presses the upload button, which sends the document to the server. The input at this time is the document file selected by the user, and the output is the document data saved on the server.

[1505] Step 2:

[1506] The server passes the uploaded document to a generative AI model that analyzes it. The generative AI model scans the document's contents to detect inconsistencies and ambiguous expressions. The input is the document data stored on the server, and the output is the analysis results (a list of inconsistencies and ambiguous expressions).

[1507] Step 3:

[1508] The server generates specific improvement proposals based on the analysis results obtained by the generative AI model. Based on the analysis results, the AI ​​model generates specific methods and content for correcting inconsistencies and unclear parts of the document. The input is the analysis results, and the output is a document that reflects the improvement proposals.

[1509] Step 4:

[1510] The server automatically categorizes the improved documents and generates relevant tags. The generated documents are classified into specific categories and have tags added to them for future searches. The input is the document with the proposed improvements, and the output is structured data with categories and tags.

[1511] Step 5:

[1512] The server stores the structured data with categories and tags in a database, which can then be quickly served to users for future searches. The input is documents with categories and tags, and the output is the document data stored in the database.

[1513] Step 6:

[1514] A user accesses the search interface using a terminal and searches for the information they need. The server extracts and provides relevant documents from the database based on the user's search query. The input is the user's search query, and the output is the relevant documents provided as search results.

[1515] Step 7:

[1516] Users input questions in real time using the system's chat interface. The server analyzes the user's question using a generative AI model and generates an optimal answer. The input is the user's question, and the output is the answer generated by the generative AI model.

[1517] Step 8:

[1518] The server passes the user's input text or voice data to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the user's emotional state and adapts the service delivery method based on the results. The input is the user's text or voice data, and the output is the emotion analysis results and a response method based on that.

[1519] For example, when an administrator inputs the prompt "Scan this document and improve the unclear sections," the generative AI model identifies the unclear areas and generates specific improvement suggestions. The improvements are saved in the database with categories and tags, and are quickly made available to users in response to searches or inquiries.

[1520] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1522] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1523] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1524] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1525] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1526] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1527] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1528] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1529] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1530] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1531] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1532] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1533] 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.

[1534] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1535] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1536] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1537] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1538] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1539] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1540] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1541] The following is further disclosed regarding the above embodiment.

[1542] (Claim 1)

[1543] A means for uploading business knowledge documents;

[1544] means for generating an AI model for analyzing uploaded documents;

[1545] A means to detect inconsistencies and unclear expressions using a generative AI model and generate specific improvement proposals;

[1546] A means to automatically categorize the analyzed documents and store them in a database;

[1547] means for providing relevant information from stored documents based on a user's search query;

[1548] A means of responding immediately to user questions in chat format;

[1549] A system including:

[1550] (Claim 2)

[1551] A means of analyzing the content of uploaded documents and listing inconsistencies or unclear expressions;

[1552] Based on the analysis results, the AI ​​generates specific improvement proposals and reflects them in the document.

[1553] 10. The system of claim 1, comprising:

[1554] (Claim 3)

[1555] The system of claim 1, further comprising a means for the generative AI model to constantly learn user feedback and reflect it in the next analysis.

[1556] "Example 1"

[1557] (Claim 1)

[1558] A means for a user to upload business knowledge data using an information processing device;

[1559] A means of receiving the uploaded data and passing it to the generative AI model for analysis;

[1560] A means to analyze data using a generative AI model, detect inconsistencies and unclear expressions, and generate specific improvement proposals;

[1561] A means to automatically categorize the analyzed data, add relevant tags, and store it as structured data in a database.

[1562] a means for quickly providing relevant information from the stored data based on a user's search query;

[1563] A means to generate answers to user questions in real time using a generative AI model and provide immediate responses in chat format;

[1564] A system including:

[1565] (Claim 2)

[1566] A means of analyzing the content of the uploaded data and listing any inconsistencies or unclear expressions;

[1567] Based on the analysis results, the AI ​​model generates specific improvement proposals and reflects them in the data.

[1568] 10. The system of claim 1, comprising:

[1569] (Claim 3)

[1570] The system of claim 1, further comprising a means for the generative AI model to constantly learn user feedback and reflect it in the next analysis.

[1571] "Application Example 1"

[1572] (Claim 1)

[1573] A means for uploading business knowledge documents;

[1574] means for generating an AI model for analyzing uploaded documents;

[1575] A means to detect inconsistencies and unclear expressions using a generative AI model and generate specific improvement proposals;

[1576] A means to automatically categorize the analyzed documents and store them in a database;

[1577] means for providing relevant information from stored documents based on a user's search query;

[1578] A means of responding immediately to user questions in chat format;

[1579] A means for scanning and uploading business knowledge documents via a smartphone or general-purpose computing device;

[1580] A means to classify business knowledge documents analyzed by the generative AI model into categories such as project management, quality control, and inventory management;

[1581] A system including:

[1582] (Claim 2)

[1583] A means of analyzing the content of uploaded documents and listing inconsistencies or unclear expressions;

[1584] Based on the analysis results, the AI ​​generates specific improvement proposals and reflects them in the document.

[1585] 10. The system of claim 1.

[1586] (Claim 3)

[1587] A means for the generative AI model to constantly learn from user feedback and reflect it in the next analysis;

[1588] 10. The system of claim 1, further comprising means for providing real-time improvement suggestions as a user enters a search query via a smartphone or general-purpose computing device.

[1589] "Example 2: Combining Emotion Engines"

[1590] (Claim 1)

[1591] A means for uploading business knowledge data;

[1592] means for generating an AI model for analyzing the uploaded data;

[1593] A means to detect inconsistencies and unclear expressions using a generative AI model and generate specific improvement proposals;

[1594] A means to automatically categorize the analyzed data and store it in a database;

[1595] means for providing relevant information from the stored data based on a user's search query;

[1596] A means of responding to user questions in real time via chat;

[1597] means for passing real-time input data from a user to an emotion engine for analyzing the emotional state;

[1598] A means for adapting the system's behavior based on the analysis results of the emotion engine and accumulating feedback;

[1599] A system including:

[1600] (Claim 2)

[1601] A means of analyzing the content of the uploaded data and listing any inconsistencies or unclear expressions;

[1602] Based on the analysis results, the AI ​​generates specific improvement proposals and reflects them in the data.

[1603] 10. The system of claim 1, comprising:

[1604] (Claim 3)

[1605] The system of claim 1, further comprising a means for the generative AI model to constantly learn user feedback and reflect it in the next analysis.

[1606] "Application example 2 when combining emotion engines"

[1607] (Claim 1)

[1608] A means for uploading business knowledge documents;

[1609] means for generating an AI model for analyzing uploaded documents;

[1610] A means to detect inconsistencies and unclear expressions using a generative AI model and generate specific improvement proposals;

[1611] A means to automatically categorize the analyzed documents and store them in a database;

[1612] means for providing relevant information from stored documents based on a user's search query;

[1613] A means of responding immediately to user questions in chat format;

[1614] means for analyzing the user's emotions and adapting a service provision method based on the user's emotions;

[1615] A system including:

[1616] (Claim 2)

[1617] A means of analyzing the content of uploaded documents and listing inconsistencies or unclear expressions;

[1618] Based on the analysis results, the AI ​​generates specific improvement proposals and reflects them in the document.

[1619] A means to automatically categorize the improved documents and generate and store associated tags in a database;

[1620] 10. The system of claim 1, comprising:

[1621] (Claim 3)

[1622] The generative AI model constantly learns from user feedback and incorporates it into the next analysis.

[1623] 10. The system of claim 1, further comprising means for performing sentiment analysis based on real-time user input data and adapting based on the user's emotional state. [Explanation of symbols]

[1624] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for uploading business knowledge documents; means for generating an AI model for analyzing uploaded documents; A means to detect inconsistencies and unclear expressions using a generative AI model and generate specific improvement proposals; A means to automatically categorize the analyzed documents and store them in a database; means for providing relevant information from stored documents based on a user's search query; A means of responding immediately to user questions in chat format; A system including:

2. A means of analyzing the content of uploaded documents and listing inconsistencies or unclear expressions; Based on the analysis results, the AI ​​generates specific improvement proposals and reflects them in the document. The system of claim 1 , comprising:

3. The system of claim 1, further comprising means for the generative AI model to constantly learn user feedback and reflect it in the next analysis.

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