System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving data related to a business input by a user; means for executing a generation model for analyzing the data and automatically generating a handover material; and means for outputting the generated handover material.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 today's corporate environment, frequent personnel transfers, job changes, and job changes are increasing the complexity and effort required for job handovers. Traditional manual handover processes consume a lot of time and resources, and can lead to business interruptions and prolonged adaptation periods. This reduces business efficiency and negatively impacts the performance of the entire company. To solve these issues, there is a need for a method to automate job handovers and ensure quick and efficient handovers. [Means for solving the problem]

[0005] This invention provides a system that analyzes business data entered by a user, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative model. Specifically, the system includes a means for receiving business data entered by a user and automatically generating and outputting handover materials using a generative model. It also includes a means for receiving past email and chat history, analyzing them, generating and outputting FAQs, and a means for receiving business procedure manuals and business flows, generating and outputting training materials. The system further includes a means for sending the user's input data to a server for analysis and generation, and a means for sending the generated materials to a terminal for provision to the user. This system makes it possible to streamline business handover and quickly switch to a new business.

[0006] "User" refers to a person or entity that uses the system.

[0007] "Input business data" refers to information such as business details, keywords, documents, etc. that users provide to the system.

[0008] "Terminal" refers to the electronic device (e.g., computer, smartphone, tablet, etc.) used by a User to access the System.

[0009] "Server" refers to a central processing system for receiving, analyzing, generating, storing, and providing data.

[0010] A "generative AI model" refers to a software algorithm that uses artificial intelligence to analyze input data and automatically generate materials.

[0011] "Handover materials" refers to documents and presentations for business handover that are automatically generated by a generative AI model.

[0012] "Past email and chat history" refers to past communication data related to work (e.g., emails, chat logs, etc.).

[0013] "Frequently Asked Questions (FAQ)" refers to a document containing frequently asked questions and their answers that is automatically generated by a generative AI model based on past communication data.

[0014] "Business procedure manuals and business flows" refer to documents that show the procedures and flow charts for business operations.

[0015] "Training materials" refers to documents and presentations automatically generated by a generative AI model to help business successors understand the job content. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[0038] An embodiment of a handover document creation system

[0039] Program processing explanation

[0040] 1. The user enters business data

[0041] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[0042] 2. The device sends the data to the server

[0043] The terminal sends business data entered or uploaded by the user to the server, including the process of packetizing the input data and sending it to the server via API.

[0044] 3. The server instructs the generative AI model

[0045] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, it passes instructions such as "Create handover documents" to the AI ​​model along with the data.

[0046] 4. Generative AI model generates handover documents

[0047] The generative AI model automatically generates conversation content and materials for handovers based on the provided business data. The generated materials are saved in Markdown format.

[0048] 5. The server sends the generated data to the device.

[0049] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[0050] 6. The user checks and edits the document

[0051] The user checks the handover materials generated on the terminal, and edits and uses them as necessary.

[0052] FAQ creation system embodiment

[0053] 1. Users upload their past email and chat history

[0054] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[0055] 2. The device sends the history data to the server

[0056] Uploaded emails and chat histories are sent from the device to the server, where the device analyzes the data and converts it into an appropriate format for transmission to the server.

[0057] 3. The server instructs the AI ​​model to generate FAQs

[0058] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers.

[0059] 4. Generative AI model generates FAQs

[0060] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers, such as "About the product warranty period" and "How to process a refund."

[0061] 5. The server sends the generated FAQ to the device.

[0062] The server sends the generated FAQ to the user's terminal, allowing the user to view the FAQ.

[0063] 6. Users check and edit the FAQ

[0064] The user can check the FAQ generated on the device and make corrections or additions as necessary.

[0065] Embodiment of training support system

[0066] 1. The user inputs or uploads a business procedure manual or workflow.

[0067] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0068] 2. The device sends business data to the server

[0069] The uploaded business data is sent from the terminal to the server, where it is packetized and sent to the server.

[0070] 3. The server instructs the AI ​​model to generate training materials.

[0071] The server provides the received business procedures and workflow to the generative AI model and instructs it to generate training materials.

[0072] 4. Generative AI models generate training materials

[0073] The generative AI model analyzes provided procedures and workflows to generate educational presentation materials and training manuals, such as "Points to remember when visiting customers" and "How to use sales report templates."

[0074] 5. The server sends the generated training materials to the device.

[0075] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[0076] 6. User confirms and provides materials

[0077] The user checks the generated training materials on the terminal, and after confirming that there are no problems, provides the materials to the new person in charge.

[0078] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[0079] The processing flow will be explained below.

[0080] Processing steps of the handover document creation system

[0081] Creating handover documents

[0082] Step 1:

[0083] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[0084] Step 2:

[0085] The device sends the entered or uploaded business data to the server, which then packets keywords and documents and sends a request to the server via API.

[0086] Step 3:

[0087] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, the server instructs the AI ​​model to "create handover documents" and provides the data to the AI ​​model.

[0088] Step 4:

[0089] The generative AI model automatically generates conversation content and materials for handovers based on business data. The AI ​​model generates a conversation scenario based on the business content and converts it into a Markdown document to create the materials.

[0090] Step 5:

[0091] The server receives the generated handover material and transmits it to the user's terminal. The server repackets the material and transmits it to the user's terminal.

[0092] Step 6:

[0093] The user checks the handover documents generated on the device, edits them as necessary, and shares them with subordinates or successors.

[0094] FAQ creation

[0095] Step 1:

[0096] The user uses a device to upload past email data and chat history related to the work to be handed over. For example, the user uploads customer support email history from the past year.

[0097] Step 2:

[0098] The device sends the uploaded email and chat history data to the server, where it analyzes the data and converts it into a data format suitable for sending to the server.

[0099] Step 3:

[0100] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the data to the AI ​​model.

[0101] Step 4:

[0102] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers. The AI ​​analyzes data patterns and generates FAQs such as "About the product warranty period" and "How to process a refund."

[0103] Step 5:

[0104] The server sends the generated FAQ to the user's terminal. The server repackets the FAQ and sends it to the user's terminal.

[0105] Step 6:

[0106] The user reviews the generated FAQ on the device, corrects or adds answers as needed, and provides it to the new support representative.

[0107] Training Support

[0108] Step 1:

[0109] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0110] Step 2:

[0111] The terminal sends the entered or uploaded procedure manual and business flow data to the server, which then packets the data and sends it to the server.

[0112] Step 3:

[0113] The server provides the received business data to the generative AI model and instructs it to generate training materials. Specifically, the server instructs the AI ​​model to "create training materials" and provides the data to the AI ​​model.

[0114] Step 4:

[0115] The generative AI model analyzes the provided procedure manuals and business flows to generate educational presentation materials and training manuals. The AI ​​analyzes the procedure manuals and business flows to generate materials such as "key points to remember when visiting customers" and "how to use sales report templates."

[0116] Step 5:

[0117] The server receives the generated training materials and sends them to the user's terminal. The server repackets the materials and sends them to the user's terminal.

[0118] Step 6:

[0119] The user checks the training materials generated on the terminal, and after checking the contents as necessary, provides the materials to the new personnel.

[0120] Through the above process, the efficiency of business handover and rapid business adaptation can be achieved.

[0121] Example 1

[0122] 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."

[0123] Currently, many companies face inefficient manual processes for handing over tasks, creating FAQs, and preparing training materials. It's also difficult to maintain the accuracy and consistency of information, making it difficult to quickly hand over tasks to new staff. This leads to reduced operational efficiency and the risk of information leaks.

[0124] 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.

[0125] In this invention, the server includes means for receiving business data entered by a user, means for transmitting the received business data to the server, means for the server to instruct the generation AI model to analyze and generate materials, means for the generation AI model to generate handover materials based on the analysis results, means for transmitting the generated handover materials from the server to the user's terminal, and means for the user to check and edit the generated materials on the terminal. This enables efficient business handover, rapid creation of FAQs, and automatic generation of training materials, enabling rapid handover to new personnel while maintaining information accuracy and consistency.

[0126] "Business Data" means information entered or uploaded by a User in the course of performing a Business, including text, documents, images, and other file formats.

[0127] A "terminal" is an electronic device operated by a user to input or upload business data, and includes a personal computer, tablet, smartphone, etc.

[0128] A "server" is a central processing unit that analyzes received business data and instructs the AI ​​model to generate materials, and has the ability to communicate with multiple terminals via a network.

[0129] A "generative AI model" is an artificial intelligence model that automatically generates handover documents, FAQs, and training materials based on input data, and analyzes data using natural language processing and machine learning techniques.

[0130] "Handover materials" are documents created by a generative AI model for the purpose of business handover, saved in Markdown format or similar, and containing an overview of the business and detailed procedures.

[0131] An "FAQ" is a collection of frequently asked questions and their answers about specific tasks or products, and is automatically generated by a generative AI model that analyzes past email and chat history.

[0132] "Training materials" are learning materials created by a generative AI model based on work procedures and workflows, and are intended to support the education of new employees and newly appointed personnel.

[0133] "Upload" refers to the act of a user sending data to a server using a terminal, and includes the operation of selecting data through a file selection or input form and pressing the send button.

[0134] "Packetization" refers to the process of dividing the data to be transmitted into a series of data packets and converting them into a format that can be efficiently transmitted over a network.

[0135] "API" is an abbreviation for Application Programming Interface, which enables data communication between servers and terminals, and sends and receives data using standardized communication protocols and data formats.

[0136] "Analysis" refers to the process by which the server or generative AI model analyzes the data it receives and extracts the necessary information, including operations such as formatting, classifying, and summarizing the data.

[0137] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[0138] System configuration

[0139] The system consists of the following major hardware and software components:

[0140] Terminal: A device such as a personal computer, tablet, or smartphone that allows a user to input and upload data.

[0141] Server: A central processing unit that analyzes data and instructs the generative AI model to generate materials.

[0142] Generative AI model: An artificial intelligence model that automatically generates handover materials, FAQs, and training materials based on business data.

[0143] Network: The infrastructure for data communication between devices and servers.

[0144] An embodiment of a handover document creation system

[0145] 1. User enters and uploads business data:

[0146] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user can input keywords such as "marketing strategy" or "advertising cost calculation" and upload related PDF files.

[0147] 2. The device sends the data to the server:

[0148] The terminal converts the data entered or uploaded by the user into the appropriate format and sends it to the server, where it is packetized and transmitted using the HTTPS protocol.

[0149] 3. The server instructs the generative AI model:

[0150] The server analyzes the received business data and instructs the AI ​​model to generate handover documents. The specific prompt is something like "Create handover documents."

[0151] 4. The generative AI model generates handover materials:

[0152] The generative AI model analyzes the provided data and automatically generates handover documents, which are saved in Markdown format.

[0153] 5. The server sends the generated data to the device:

[0154] The server compiles the handover materials received from the generative AI model and sends them to the user's device, where the user can view the generated materials.

[0155] 6. User reviews and edits the document:

[0156] The user can check the generated handover documents on the device and edit them as necessary. The device is provided with a Markdown editor and preview function.

[0157] FAQ creation system embodiment

[0158] 1. User uploads past email and chat history:

[0159] Users can use their devices to upload past email and chat history, for example, uploading customer support email history.

[0160] 2. The device sends the history data to the server:

[0161] The device converts the uploaded data into an appropriate format and sends it to the server.

[0162] 3. The server instructs the generative AI model to generate the FAQ:

[0163] The server analyzes the received data and instructs the generative AI model to generate an FAQ, using prompts such as "Generate an FAQ."

[0164] 4. The generative AI model generates the FAQ:

[0165] The generative AI model analyzes historical data and automatically generates FAQs and their answers, such as questions like "What is the product warranty period?"

[0166] 5. The server sends the generated FAQ to the device:

[0167] The server compiles the generated FAQs and sends them to the user's terminal.

[0168] 6. User checks and edits FAQ:

[0169] The user checks the generated FAQ on the terminal and makes corrections as necessary.

[0170] Embodiment of training support system

[0171] 1. User uploads a procedure or workflow:

[0172] Users use the terminal to input or upload work procedures and workflow diagrams. For example, they can upload procedures such as "procedures for visiting customers" and "how to write sales reports."

[0173] 2. The device sends business data to the server:

[0174] The uploaded data is sent from the terminal to the server.

[0175] 3. The server instructs the generative AI model to generate training materials:

[0176] The server analyzes the received data and instructs the generative AI model to generate training materials.

[0177] 4. The generative AI model generates training materials:

[0178] The generative AI model analyzes procedures and workflows to automatically generate training materials, such as "key points to remember when visiting customers."

[0179] 5. The server sends the generated training materials to the device:

[0180] The server compiles the generated training materials and sends them to the user's terminal.

[0181] 6. User confirms and provides materials:

[0182] The user checks the training materials generated on the terminal, verifies that there are no problems, and then provides them to the new person in charge.

[0183] Example prompt

[0184] 1. When creating handover documents:

[0185] Please compile past business data related to "marketing strategies" and "advertising cost calculations" and prepare handover materials.

[0186] 2. For creating FAQs:

[0187] Generate a list of frequently asked questions and answers based on the past year's worth of customer support emails.

[0188] 3. For training materials:

[0189] Create training materials based on the procedures for "Customer Visit Procedures" and "How to Write a Sales Report."

[0190] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[0191] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0192] Processing steps of the handover document creation system

[0193] Step 1:

[0194] Users use their devices to input or upload business data keywords and related documents. Specifically, they enter keywords such as "marketing strategy" or "advertising cost calculation" into a text box through the device's web interface, and select the related PDF file from the file upload dialog. The input here is the keywords and PDF file, and the output is the data stored on the device.

[0195] Step 2:

[0196] The terminal packets business data entered by the user and sends it to the server. Specifically, a script running on the terminal reads the entered text data or uploaded files and converts it into an appropriate format, such as JSON. The data is then sent to the server via an API using HTTPS. The input here is JSON-formatted data, and the output is that data being sent to the server.

[0197] Step 3:

[0198] The server analyzes the received business data and instructs the generative AI model to create handover documents. Specifically, the server formats and validates the data, checking for inappropriate data. After analyzing the data, it sends a request to the generative AI model along with a prompt such as "Create handover documents." The input is the processed business data and the prompt, and the output is a request sent to the generative AI model.

[0199] Step 4:

[0200] The generative AI model generates handover materials based on the received data. Specifically, the generative AI model analyzes business data, extracts relevant information, and compiles it into a Markdown-formatted document. For example, the "Marketing Strategy" section contains content based on past cases and successful strategies. The input is business data and prompt text, and the output is the generated Markdown-formatted handover materials.

[0201] Step 5:

[0202] The server sends the handover materials received from the generative AI model to the user's device. Specifically, the generated materials are linked to the user ID and session information, stored in a temporary file, and notified to the user's device via a notification API. The notification includes a download link for the materials. The input is the generated materials, and the output is a notification sent to the user's device.

[0203] Step 6:

[0204] The user checks and edits the handover materials generated on the device. Specifically, the user checks the content using the Markdown editor and preview function displayed on the device and makes corrections as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated material, and the output is the corrected final material.

[0205] FAQ creation system processing steps

[0206] Step 1:

[0207] The user uploads past email and chat history using the device. Specifically, the user selects and uploads the past year's worth of email history from the "Customer Support" folder through the device's file upload dialog. The input is an email history file, and the output is the history data stored on the device.

[0208] Step 2:

[0209] The terminal packetizes the uploaded history data and sends it to the server. Specifically, a script on the terminal parses the history data and converts it into an appropriate format such as CSV or JSON. It then sends it to the server via API using the HTTPS protocol. The input is the converted history data, and the output is the data sent to the server.

[0210] Step 3:

[0211] The server analyzes the received history data and instructs the generative AI model to generate FAQs. Specifically, the server organizes the history data by theme and sends a request to the generative AI model along with a prompt such as "Generate an FAQ." The input is the organized history data and the prompt, and the output is a request sent to the generative AI model.

[0212] Step 4:

[0213] The generative AI model generates FAQs based on historical data. Specifically, the generative AI model analyzes historical data and automatically generates frequently asked questions and their answers. For example, a question and answer about the "warranty period of a product" is created. The input is historical data and prompt text, and the output is the generated FAQ.

[0214] Step 5:

[0215] The server sends the generated FAQ to the user's device. The generated FAQ is stored as a temporary file, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated FAQ, and the output is the notification sent to the user's device.

[0216] Step 6:

[0217] The user checks and edits the generated FAQ on the device. Specifically, the user checks the questions and answers in the FAQ editing interface displayed on the device, and makes corrections or additions as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated FAQ, and the output is the final, modified FAQ.

[0218] Training support system processing steps

[0219] Step 1:

[0220] Users use their devices to upload work procedures and workflows. Specifically, they select and upload procedures such as "Procedures for visiting customers" or "How to write sales reports" from the device's file upload dialog. The input is the procedure file, and the output is data stored on the device.

[0221] Step 2:

[0222] The terminal packetizes the uploaded business data and sends it to the server. Specifically, a script on the terminal analyzes the business data, converts it into an appropriate format, and sends it to the server using the HTTPS protocol. The input is the converted business data, and the output is the data sent to the server.

[0223] Step 3:

[0224] The server analyzes the received business data and instructs the generative AI model to generate training materials. Specifically, the server analyzes the procedure manual and business flow and sends a request to the generative AI model along with a prompt. The input is the procedure manual data and the prompt, and the output is a request sent to the generative AI model.

[0225] Step 4:

[0226] The generative AI model generates training materials based on the data it receives. Specifically, the generative AI model analyzes procedures and business flows, and automatically generates educational presentation materials and training manuals. For example, it creates materials such as "Points to remember when visiting customers" and "How to use sales report templates." The input is business data and prompts, and the output is the generated training materials.

[0227] Step 5:

[0228] The server sends the generated training materials to the user's device. The generated materials are stored as temporary files, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated training materials, and the output is a notification of the sending to the user's device.

[0229] Step 6:

[0230] The user checks and provides the generated training materials on the terminal. Specifically, the user checks the training materials on the terminal, confirms that there are no problems, and then prepares to provide them to the new person in charge. The user reviews the materials and checks for errors or omissions. The input is clicking a link in the generated training materials, and the output is the final, confirmed training materials.

[0231] (Application example 1)

[0232] 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."

[0233] In modern manufacturing, rapid training and efficient implementation of new work procedures and workflows are key challenges. In particular, in-factory worker training often relies on manual explanations and training, which requires time and effort. Furthermore, there is a lack of technological means to efficiently manage work-related data and automatically generate handover and training materials based on that data. This creates a need for rapid and accurate training of new work procedures and workflows.

[0234] 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.

[0235] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and executing a generative model for automatically generating handover materials, means for outputting the generated handover materials, means for a factory worker to input or upload work procedures and workflows to a robot, means for the robot to send data to a central server, means for the central server to instruct the generative AI model to generate training materials, means for the generative AI model to generate training materials, means for the central server to send the generated training materials to the robot, and means for the robot to display the materials and for the worker to review and use. This enables factory workers to efficiently receive training on new work procedures and workflows.

[0236] "Business Data" refers to business-related information, documents, procedures, or business flows that are entered or uploaded by a user.

[0237] A "generative model" refers to an algorithm or machine learning model that analyzes data entered by a user and automatically generates materials and answers.

[0238] A "generative AI model" refers to an advanced model that uses artificial intelligence to analyze data entered by users and automatically generate materials.

[0239] "Handover materials" refers to documents that compile the information and procedures necessary to hand over work to another person in charge.

[0240] "Central Server" refers to a central computing device for remote data analysis and management.

[0241] "Training Materials" means educational documents and materials used to learn new business procedures and workflows.

[0242] A "business procedure manual" is a document that contains specific procedures and instructions for carrying out a specific task.

[0243] "Business flow" refers to a flowchart or diagram that shows the series of processes and steps by which a specific business is carried out.

[0244] A "robot" is an automated machine used in factories that has the ability to analyze data entered by workers and display materials.

[0245] A "display screen" refers to a display device installed on a robot or terminal, which visually displays generated materials and information.

[0246] "Factory workers" refer to employees who are engaged in tasks such as machine operation and assembly work within a factory.

[0247] This invention is a system that supports efficient training and job handover for factory workers. Specifically, the system allows users to provide work procedures and workflows, and a central server automatically generates training materials using a generative AI model based on these. The materials are then displayed by a robot.

[0248] Program processing explanation

[0249] Hardware and software used

[0250] Robot terminal: Robots used in factories are equipped with a display screen that provides an interface for workers to input work procedures and workflows.

[0251] Central server: A computing device for data management and processing that processes received business data and instructs the generative AI model on how to analyze it. Specific software includes a RESTful API for transferring data via API.

[0252] Generative AI models: Use advanced artificial intelligence models, such as OpenAI's GPT-4, to analyze the information you provide and automatically generate training and handover materials.

[0253] Specific program behavior

[0254] 1. Data entry: Factory workers use robotic terminals to input or upload new work procedures and workflows, such as "new assembly line procedures" or "machine maintenance workflows."

[0255] 2. Data transmission: The robot terminal transmits the entered or uploaded data to the central server. The data is transmitted using an HTTP POST request.

[0256] 3. Data analysis and material generation: The central server provides the received data to the generative AI model and instructs it to generate training materials. The generative AI model automatically generates training materials based on the data provided by the user.

[0257] 4. Material transmission and display: The generated training materials are sent back to the robot terminal by the central server, and the robot terminal displays the materials on the display screen for factory workers to review and use.

[0258] Adding specific examples

[0259] Prompt Sentence Examples

[0260] Please generate training materials based on the following operating procedures:

[0261] 1. Assembly line preparation

[0262] 2. Safety Check

[0263] 3. Machine start-up procedure

[0264] 4. Production line operation method

[0265] …

[0266] By providing the above prompts to the generative AI model, the model can analyze the user's work procedures and automatically generate relevant training materials, allowing factory workers to quickly master the new work procedures.

[0267] As described above, the present invention is a system that utilizes a generative AI model based on business data provided by the user to automatically generate and efficiently display business handover and training materials, thereby improving business efficiency and enabling rapid adaptation to new business operations.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] Users use the robot terminal to input or upload work procedures and workflows. The input data includes specific details of the procedures and workflows, such as "new assembly line procedures" or "machine maintenance flow," in text or PDF format.

[0271] Step 2:

[0272] The robot terminal sends the input or uploaded business data to the central server. To do this, the robot terminal uses an HTTP POST request to send the data to the server via an API. The input data is packetized, converted into the appropriate format, and then sent.

[0273] Step 3:

[0274] The server provides the received data to the generative AI model and instructs it to generate training materials. The server sends the data to the generative AI model using an HTTP request, including a prompt such as "Please generate training materials based on the following business procedure manual."

[0275] Step 4:

[0276] The generative AI model analyzes the provided data and automatically generates training materials. The generative AI model creates specific training materials and manuals based on the work procedures entered by the user. For example, it generates materials including content such as "assembly line preparation," "safety checks," and "machine startup procedures."

[0277] Step 5:

[0278] The server sends the generated training materials to the robot terminal, converts the materials received from the generative AI model into an appropriate format, and sends them back to the robot terminal using an HTTP POST request.

[0279] Step 6:

[0280] The robot terminal displays the generated training materials on the display screen for the user to review and use. The robot terminal then displays the received materials on the display screen, allowing factory workers to review work procedures based on the materials and apply them to their actual work.

[0281] 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.

[0282] This invention analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine.By using the emotion engine, it is possible to analyze user emotions and reflect them in the generated materials.

[0283] An embodiment of a handover document creation system

[0284] Program processing explanation

[0285] 1. The user enters business data

[0286] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[0287] 2. Emotion engine recognizes emotions

[0288] The emotion engine recognizes the emotion of the user as they type. For example, the emotion engine analyzes the emotions (joy, anger, sadness, etc.) that the user is expressing while typing text.

[0289] 3. The device sends the data to the server

[0290] The device sends the input business data and the recognized emotion data to the server, packetizes the data, and sends a request to the server via API.

[0291] 4. The server instructs the generative AI model

[0292] The server analyzes the received business data and emotional data and issues instructions to the AI ​​model to generate documents. Specifically, it instructs the AI ​​model to "create handover documents," and also provides the emotional data to the AI ​​model.

[0293] 5. Generative AI model generates handover documents

[0294] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotional data. The AI ​​model adjusts the conversation scenario to reflect the emotional data, and converts it into a Markdown-formatted document to create the materials.

[0295] 6. The server sends the generated data to the terminal.

[0296] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[0297] 7. The user checks and edits the document

[0298] The user checks the handover materials generated on the terminal and edits and uses them as necessary, for example, by adding additional information to the materials or adjusting the layout.

[0299] FAQ creation system embodiment

[0300] 1. Users upload their past email and chat history

[0301] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[0302] 2. Emotion engine recognizes emotions

[0303] The emotion engine recognizes user emotions from email and chat history, for example, extracting emotions (e.g., customer dissatisfaction or satisfaction) within each email or chat.

[0304] 3. The device sends the history data to the server

[0305] Uploaded email and chat history data and recognized emotion data are sent from the device to the server, where the data is analyzed and converted into a data format suitable for sending to the server.

[0306] 4. The server instructs the generative AI model to generate FAQs

[0307] The server provides the received history data and emotion data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the emotion data to the model.

[0308] 5. Generative AI model generates FAQs

[0309] The generative AI model analyzes the provided historical and sentiment data to automatically generate frequently asked questions and their answers. For example, based on customer sentiment analysis, it generates FAQs such as "About the product warranty period" and "How to process a refund."

[0310] 6. The server sends the generated FAQ to the device.

[0311] The server sends the generated FAQ and its answers to the user's terminal, where the data is repacketized and sent to the user's terminal.

[0312] 7. Users check and edit the FAQ

[0313] The user checks the FAQ generated on the terminal, makes corrections or additions as necessary, and provides it to the new support staff.

[0314] Embodiment of training support system

[0315] 1. The user inputs or uploads a business procedure manual or workflow.

[0316] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0317] 2. Emotion engine recognizes emotions

[0318] The emotion engine recognizes the user's emotions in the procedure manual or workflow. For example, it analyzes the emotions (satisfaction, anxiety, etc.) when creating the procedure manual.

[0319] 3. The device sends business data to the server

[0320] The uploaded business data and recognized emotion data are sent from the device to the server, which then packets the data and sends it to the server.

[0321] 4. The server instructs the AI ​​model to generate training materials.

[0322] The server provides the received business data and emotional data to the generative AI model and instructs it to generate training materials. Specifically, it instructs the AI ​​model to "create training materials" and provides the emotional data to the model.

[0323] 5. Generative AI models generate training materials

[0324] The generative AI model analyzes business data and emotional data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[0325] 6. The server sends the generated training materials to the device.

[0326] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[0327] 7. User confirms and provides materials

[0328] The user checks the training materials generated on the terminal, checks the contents as necessary, and then provides the materials to the new person in charge.

[0329] As described above, this invention utilizes a generative AI model and an emotion engine to automatically generate materials based on data and emotion data entered by the user, and provides the results to the user, thereby streamlining business handover and enabling rapid adaptation to new business operations.

[0330] The processing flow will be explained below.

[0331] Processing steps of the handover document creation system

[0332] Creating handover documents

[0333] Step 1:

[0334] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[0335] Step 2:

[0336] The emotion engine recognizes the user's emotions. The emotion engine analyzes emotions such as "stress" and "expectation" from the text entered by the user and the contents of related documents.

[0337] Step 3:

[0338] The device sends the input business data and emotion data to the server. Specifically, the encoded data is packetized and sent to the server via an API.

[0339] Step 4:

[0340] The server provides the received business data and emotion data to the generative AI model and instructs it to generate documents. The server then sends the data to the AI ​​model along with the instruction to "create handover documents."

[0341] Step 5:

[0342] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotion data. The materials are created in Markdown format and include explanations and points of caution that reflect the emotion data.

[0343] Step 6:

[0344] The server sends the generated handover materials to the terminal. The server compiles the materials, packets them into a format that is easy for the user to understand, and sends them to the terminal.

[0345] Step 7:

[0346] The user checks the generated handover documents on the terminal and edits them as necessary, for example, by correcting the text of the documents or adding additional information.

[0347] FAQ creation

[0348] Step 1:

[0349] The user uses a device to upload past email data and chat history related to the work being handed over. For example, upload the past year's worth of customer support email history.

[0350] Step 2:

[0351] The emotion engine recognizes user emotions from email and chat history, extracting emotions such as "satisfied" or "dissatisfied" from customer interactions.

[0352] Step 3:

[0353] The device sends the uploaded history data and emotion data to the server, which analyzes the encoded data, converts it into a format suitable for transmission, and sends it to the server.

[0354] Step 4:

[0355] The server instructs the generation AI model to generate an FAQ. The server instructs the AI ​​model to "create an FAQ" and provides historical data and emotional data to the AI ​​model.

[0356] Step 5:

[0357] Generative AI models analyze historical and sentiment data to automatically generate frequently asked questions (FAQs) and their answers, for example, by taking customer sentiment into account.

[0358] Step 6:

[0359] The server sends the generated FAQ to the terminal. The server compiles the information, packets it into an appropriate format, and sends it to the user's terminal.

[0360] Step 7:

[0361] The user checks the generated FAQ on the device and makes corrections or additions as necessary, for example, adding new questions or making changes to clarify the answers.

[0362] Training Support

[0363] Step 1:

[0364] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, they can upload procedure manuals such as "procedures for visiting customers" or "how to write sales reports."

[0365] Step 2:

[0366] The emotion engine recognizes the user's emotions from the procedure manual and business flow, analyzing emotions such as "sense of relief" and "confusion" from the contents and explanations of the procedure manual.

[0367] Step 3:

[0368] The device sends business data and emotion data to the server, which encodes the data into packets and sends them to the server via API.

[0369] Step 4:

[0370] The server instructs the AI ​​model to generate training materials. The server instructs the AI ​​model to "create training materials" and provides data and emotional information to the AI ​​model.

[0371] Step 5:

[0372] The generative AI model analyzes business data and emotion data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[0373] Step 6:

[0374] The server sends the generated training materials to the terminal, where it compiles the materials, packets them into an appropriate format for delivery to the user, and transmits them.

[0375] Step 7:

[0376] The user checks the training materials generated on the terminal, and after reviewing the content as necessary, provides the materials to the new personnel, for example by editing the materials to add additional explanations.

[0377] Through this process, handover materials, FAQs, and training materials that reflect the user's feelings are automatically generated. This system makes the handover process more efficient and enables rapid business adaptation.

[0378] Example 2

[0379] 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."

[0380] In today's business environment, the creation of efficient handover documents, frequently asked questions (FAQs), and training materials is a key challenge. However, creating these documents manually requires a significant amount of time and effort, and often loses emotional nuance and context. It is also difficult to effectively utilize past communications and operating procedures. Therefore, there is a need for automated, efficient, and emotionally sensitive document generation.

[0381] 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.

[0382] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and recognizing emotions, means for transmitting the input data and emotional data from the user terminal to the server, means for automatically generating handover materials, FAQs, and training materials using a generative AI model based on the received data and emotional data, and means for transmitting and outputting the generated materials to the user terminal. This improves the efficiency of automatic generation of work-related materials and enables the creation of more appropriate materials that take user emotions into consideration.

[0383] "Data related to business" refers to information related to business input by a user, and includes all data necessary for business execution, including text, documents, keywords, and the like.

[0384] An "emotion engine" is an algorithm that analyzes text and data entered by users and recognizes and classifies emotions.

[0385] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning techniques to automatically generate specific materials from input data.

[0386] "Handover materials" are documents and guides created to efficiently and accurately hand over work to another person.

[0387] "Frequently Asked Questions (FAQ)" refers to a collection of frequently asked questions and answers from users regarding a particular business or service.

[0388] "Training materials" are educational materials used to enable new personnel and staff to carry out their duties smoothly, and include procedures and workflow diagrams.

[0389] A "server" is a device that receives, analyzes, stores, and transmits data over a network, and in this invention also executes the generative AI model.

[0390] "Terminal" refers to a device that allows a user to input, send, and receive business data and history data, and display the generated materials.

[0391] "Receiving means" refers to a function or process for receiving data entered by a user.

[0392] "Transmission means" refers to a function or process for transmitting received data or generated materials to a server or user terminal.

[0393] "Output means" refers to the functions and processes for providing the generated materials in a format that can be displayed and edited on the user's terminal.

[0394] "Interface" refers to the operation screen or input means that allows users to input business data and check and edit generated documents.

[0395] This invention is a system that uses business data and past communication history entered by users to automatically generate handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine. The implementation method is described in detail below.

[0396] System Configuration

[0397] This system consists of a terminal operated by the user, a server that analyzes and processes data, a generative AI model, and an emotion engine. The terminal is a device into which the user inputs business data, past emails, chat history, etc., and includes PCs and tablets. The server is a central processing unit that analyzes the data and runs the generative AI model. The emotion engine is software that analyzes emotions from user input data and uses NLP (Natural Language Processing) algorithms.

[0398] Generate handover documents

[0399] 1. Input: The user uses the terminal to input business data, either manually using text fields or by uploading relevant documents using drag-and-drop. For example, they can upload keywords such as "marketing strategy" and "advertising cost calculation" and relevant documents.

[0400] 2. Sentiment analysis: The emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) from the input text in real time. This allows not only the text itself but also the user's emotions to be obtained as data.

[0401] 3. Data transmission: The device packets the input business data and recognized emotion data and sends them to the server via API. For example, the data is sent in JSON format using an HTTP request.

[0402] 4. Document generation: The server issues instructions to the AI ​​model to generate documents based on the received data. The prompt text includes specific instructions and emotional data. For example, it might say, "Create handover documents, taking into account the emotional data."

[0403] 5. Output: The generative AI model generates handover materials, converts them into Markdown documents, and sends them back to the server. The server then compiles them and sends them back to the device. The user can review these documents and edit them as needed.

[0404] FAQ generation

[0405] 1. Input: The user uploads past email and chat history from their device. For example, they upload customer support email history from the past year.

[0406] 2. Sentiment Analysis: The sentiment engine analyzes the content of emails and chats to identify the sentiment within each message.

[0407] 3. Data transmission: The device transmits history data and emotion data to the server.

[0408] 4. FAQ generation: The server instructs the AI ​​model to generate FAQs. For example, it uses a prompt such as "Generate frequently asked questions and their answers from past emails."

[0409] 5. Output: The generative AI model creates FAQs, which are then compiled by the server and sent to the user's device. The user can then review the generated FAQs and edit them as necessary.

[0410] Generate training materials

[0411] 1. Input: The user inputs the work procedure manual or workflow diagram from the terminal or uploads it in PDF format. For example, they can upload "procedures for visiting customers" or "how to write sales reports."

[0412] 2. Sentiment Analysis: The emotion engine analyzes emotions in procedures and flow diagrams. For example, it analyzes "anxiety" from the text in a procedure manual.

[0413] 3. Data transmission: The device transmits the business data and the recognized emotion data to the server.

[0414] 4. Material generation: The server instructs the AI ​​model to generate training materials, for example, by using a prompt such as "Create training materials."

[0415] 5. Output: The generative AI model generates training materials, which are then compiled by the server and sent to the user's device. The user can review the materials and edit them as necessary before providing them to the new person in charge.

[0416] Examples of specific examples and prompts

[0417] As a concrete example, consider the case where a user is creating handover documents for a "marketing strategy." When the user uploads the relevant documents, the emotion engine recognizes "joy," and the server sends a prompt to the generative AI model instructing it to "create handover documents, taking into account the emotion data." The generated documents are returned to the terminal in Markdown format, where the user can review and edit them.

[0418] Example prompt sentence:

[0419] "Create a handover document for your marketing strategy. Consider sentiment data."

[0420] In this way, the present invention is a system that enables efficient creation of materials that take emotions into consideration based on business data and emotion data.

[0421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0422] Step 1:

[0423] The user enters business data

[0424] The user uses the device to input keywords and related documents related to the business to be handed over or upload them by drag and drop. Specifically, the user may input keywords such as "marketing strategy" or "calculating advertising costs" into the text field on the device, or upload a PDF file of a marketing report. The input of this action is business data, and the output is business data saved on the device.

[0425] Step 2:

[0426] Emotion engine recognizes emotions

[0427] The device provides input business data to the emotion engine, which analyzes the emotions in the text. The emotion engine uses NLP algorithms to extract emotions such as "joy," "anger," and "sadness" from the input text. For example, if a user types "This project is great," the emotion engine recognizes "joy." The input for this operation is business data, and the output is the recognized emotion data.

[0428] Step 3:

[0429] The device sends the data to the server

[0430] The device packets the business data and the recognized emotion data and sends them to the server using an HTTP request. The data is packetized in JSON format and sent to the server's API endpoint. For example, the following JSON format data is sent:

[0431] json

[0432] {

[0433] "Business Data": "Marketing Strategy",

[0434] "Emotion Data": "Joy"

[0435] }

[0436] The inputs for this operation are task data and emotion data, and the output is the data sent to the server.

[0437] Step 4:

[0438] The server instructs the generative AI model

[0439] The server analyzes the received business data and emotion data and issues instructions to the generative AI model to generate materials. Specifically, it generates a prompt sentence and instructs the generative AI model to "create handover materials, taking into account the emotion data." For example, the generated prompt sentence is as follows:

[0440] "Create a handover document for your marketing strategy. Consider sentiment data."

[0441] The inputs for this behavior are incoming data and emotion data, and the output is instructions to the generative AI model.

[0442] Step 5:

[0443] Generative AI model generates handover documents

[0444] The generative AI model automatically generates handover materials based on the provided business data and emotion data. The generative AI model uses deep learning to analyze the input data and generate handover materials in Markdown format. For example, a document containing detailed explanations and procedures for a "marketing strategy" is generated. The input for this operation is a prompt and data, and the output is the generated handover materials.

[0445] Step 6:

[0446] The server sends the generated data to the terminal.

[0447] The server collects the handover data received from the generative AI model, repackets it, and sends it to the user's device. The data is then repacketized in an HTTP request and sent to the user's device. For example, the following Markdown-formatted data is sent:

[0448] markdown

[0449] Marketing Strategy

[0450] This article will introduce you to the key points of your marketing strategy...

[0451] The input of this operation is the generated handover material, and the output is the material sent to the user terminal.

[0452] Step 7:

[0453] Users can view and edit documents

[0454] The user reviews the handover materials generated on their device and makes edits as needed. They use document editing software or a web application to add content to the materials or adjust their layout. For example, they may add specific figures or graphs related to a marketing strategy. The input for this operation is the received handover materials, and the output is the edited final materials.

[0455] (Application example 2)

[0456] 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."

[0457] In today's business environment, efficient work handovers and improved customer support quality are required. However, traditional manual handover materials and FAQ generation require a significant amount of time and effort, and are difficult to consider individual emotions. Furthermore, preparing training materials is also a significant burden, making it difficult for new employees to quickly adapt to their work. To solve these problems, a system is needed that automatically analyzes information such as business data and customer interaction history, and generates optimal materials based on emotional data.

[0458] 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.

[0459] In this invention, the server includes a means for receiving data related to work input by a user, a means for analyzing emotions based on the received data, a means for analyzing the data and emotions and executing a generative model for automatically generating handover materials, and a means for outputting the generated handover materials. This makes it possible to create handover materials efficiently and with consideration for emotions based on the work data and emotion data.

[0460] The server also includes a means for receiving past communication history input by the user, a means for analyzing emotions from the received history data, a means for executing a generative model for automatically generating frequently asked questions and their answers based on the analyzed emotions, and a means for outputting the generated frequently asked questions and their answers. This makes it possible to generate optimal FAQs based on past customer interaction history and analyzed emotion data.

[0461] The server further includes a means for receiving a work procedure manual or work flow input by a user, a means for analyzing emotions from the received data, a means for executing a generative model for automatically generating training materials based on the analyzed data and emotions, and a means for outputting the generated training materials, thereby enabling efficient creation of training materials that enable new employees to quickly adapt to their work.

[0462] "Business data" is a general term for information, documents, instructions, procedures, etc. used by users when performing their business.

[0463] "Means for analyzing emotions" refers to a system that extracts emotional elements from user input data and history data, and classifies, evaluates, and analyzes the type and intensity of emotions.

[0464] A "generative model for generating handover materials" is a model used to automatically generate the materials necessary for the next worker to take over the work, based on the received work data and emotion data.

[0465] "Means for outputting generated handover materials" refers to means for providing the handover materials created by the generative model to the user, and refers to outputting them as electronic data or printed materials.

[0466] "Communication history" is a general term for records of past communications such as emails and chats, and includes messages and related information exchanged between a user and other people.

[0467] A "generative model for generating frequently asked questions and their answers" is a model used to automatically generate FAQs (frequently asked questions) and their answers based on communication history and emotional data.

[0468] "Means for outputting the generated frequently asked questions and their answers" refers to the means for providing the FAQs created by the generative model and their answers to users, and refers to outputting them as electronic data or printed material.

[0469] A "work procedure manual" is a document that describes the specific procedures for carrying out a specific task.

[0470] A "business flow" is a diagram or chart that visually represents the flow or process of a business.

[0471] A "generative model for generating training materials" is a model used to automatically generate materials necessary for training new employees, etc., based on business data and emotion data.

[0472] "Means for outputting generated training materials" refers to means for providing training materials created by a generative model to users, and refers to outputting them as electronic data or printed materials.

[0473] MODE FOR CARRYING OUT THE INVENTION

[0474] This invention relates to a business support system that handles business data, past communication history, business procedure manuals, and business flows. Specifically, it is a system that analyzes data entered by users and automatically generates handover documents, FAQs, and training materials using a generative AI model and an emotion engine.

[0475] System Configuration

[0476] The system includes the following main components:

[0477] 1. User terminal: Provides an interface for inputting or uploading business data, past communication history, business procedures, and business flows. For example, devices such as PCs, smartphones, and tablets are used.

[0478] 2. Server: This is the central system that receives data, parses it, analyzes sentiment, runs generative AI models, and distributes the generated materials.

[0479] 3. Emotion engine: Software for analyzing emotions from incoming and historical data. For example, the transformers library and its emotion analysis module are used.

[0480] 4. Generative AI model: An AI model for generating handover materials, FAQs, and training materials based on the received data and sentiment data. For example, OpenAI's text-davinci-003 model is used.

[0481] Program processing description

[0482] The server performs the following process:

[0483] 1. Data reception and preprocessing: The system receives business data, communication history, business procedures, and business flows entered by the user from their terminal. The user terminal converts these into a data format and sends them to the server.

[0484] 2. Sentiment Analysis: The received data is analyzed by the emotion engine to extract the emotional components in the data. For example, the user's input is classified as "positive," "negative," or "neutral."

[0485] 3. Execution of the generative AI model: Based on the results of the sentiment analysis and business data, the generative AI model generates handover materials, FAQs, and training materials. For example, it issues instructions using prompt sentences like the following:

[0486] text

[0487] Context: {Past data and historical information}

[0488] User Input: {User's specific question or request}

[0489] Emotion: {Parsed emotion label}

[0490] Generate a helpful response with appropriate sentiment.

[0491] 4. Creation and distribution of materials: The created materials are sent to the user's terminal via the server again, allowing the user to view, edit, and use them.

[0492] Specific examples

[0493] For example, if a user enters a comment such as "I can't set up the product properly, it's stressful," the system will operate as follows:

[0494] 1. Receive the data entered by the user and assign the emotion label "negative."

[0495] 2. Incorporate emotion labels and input data into prompt sentences.

[0496] text

[0497] Context: Past customer support history

[0498] User Input: I'm having trouble setting up my product. What should I do?

[0499] Emotion: Negative

[0500] Generate a helpful response with appropriate sentiment.

[0501] 3. The generative AI model generates an appropriate response and provides it to the user device via the server.

[0502] In this way, the system can analyze emotions and automatically generate optimal materials based on the user's work data, communication history, work procedures, and workflow. This not only dramatically improves the efficiency of work handovers and customer support, but also automates the creation of training materials to help new employees quickly adapt to their work.

[0503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0504] Step 1:

[0505] Inputting and receiving data via a user terminal

[0506] The user uses the device to input or upload business data, past communication history, work procedures, or work flows. For example, they can upload data about "product configuration problems," past email and chat history, and work procedures. The device receives this data and converts it into the appropriate data format.

[0507] Step 2:

[0508] Data transmission to server and pre-processing

[0509] The device converts data entered or uploaded by the user into packets and sends them to the server via API. Specifically, it sends converted data including business data, communication history, procedure manuals, etc. The server receives this data and stores it in the appropriate storage.

[0510] Step 3:

[0511] Performing sentiment analysis

[0512] The server passes the received data to the emotion engine for emotion analysis. For example, it uses the transformers library to extract and classify emotion elements (e.g., positive, negative, neutral) from the data. The analysis results in emotion labels and their intensities.

[0513] Step 4:

[0514] Prompt generation for generative AI models

[0515] The server generates a prompt sentence to be passed to the generative AI model based on the emotion analysis results and the received data. As a specific example, the following prompt sentence is created:

[0516] text

[0517] Context: Past customer support history

[0518] User Input: I'm having trouble setting up my product. What should I do?

[0519] Emotion: Negative

[0520] Generate a helpful response with appropriate sentiment.

[0521] Step 5:

[0522] Execution of generative AI models and data generation

[0523] The server uses the generated prompt to run a generative AI model (e.g., OpenAI's text-davinci-003), which generates appropriate handover materials, FAQs, or training materials based on the prompt. The generated results are output in text format.

[0524] Step 6:

[0525] Distribution of generated materials

[0526] The server repackets the output from the generative AI model and sends it to the user's device, where the delivered materials can be viewed, edited, and output.

[0527] Step 7:

[0528] User confirmation and use of materials

[0529] The user reviews the received documents on their device, edits the content or adds additional information as needed, and then prints or digitally provides the documents to other personnel. For example, handover documents are likely to include "Troubleshooting Procedures for Configuration Issues" and answers to frequently asked questions.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] [Second embodiment]

[0534] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0535] 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.

[0536] 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).

[0537] 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.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] In the smart glasses 214, the 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.

[0545] 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."

[0546] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[0547] An embodiment of a handover document creation system

[0548] Program processing explanation

[0549] 1. The user enters business data

[0550] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[0551] 2. The device sends the data to the server

[0552] The terminal sends business data entered or uploaded by the user to the server, including the process of packetizing the input data and sending it to the server via API.

[0553] 3. The server instructs the generative AI model

[0554] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, it passes instructions such as "Create handover documents" to the AI ​​model along with the data.

[0555] 4. Generative AI model generates handover documents

[0556] The generative AI model automatically generates conversation content and materials for handovers based on the provided business data. The generated materials are saved in Markdown format.

[0557] 5. The server sends the generated data to the device.

[0558] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[0559] 6. The user checks and edits the document

[0560] The user checks the handover materials generated on the terminal, and edits and uses them as necessary.

[0561] FAQ creation system embodiment

[0562] 1. Users upload their past email and chat history

[0563] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[0564] 2. The device sends the history data to the server

[0565] Uploaded emails and chat histories are sent from the device to the server, where the device analyzes the data and converts it into an appropriate format for transmission to the server.

[0566] 3. The server instructs the AI ​​model to generate FAQs

[0567] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers.

[0568] 4. Generative AI model generates FAQs

[0569] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers, such as "About the product warranty period" and "How to process a refund."

[0570] 5. The server sends the generated FAQ to the device.

[0571] The server sends the generated FAQ to the user's terminal, allowing the user to view the FAQ.

[0572] 6. Users check and edit the FAQ

[0573] The user can check the FAQ generated on the device and make corrections or additions as necessary.

[0574] Embodiment of training support system

[0575] 1. The user inputs or uploads a business procedure manual or workflow.

[0576] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0577] 2. The device sends business data to the server

[0578] The uploaded business data is sent from the terminal to the server, where it is packetized and sent to the server.

[0579] 3. The server instructs the AI ​​model to generate training materials.

[0580] The server provides the received business procedures and workflow to the generative AI model and instructs it to generate training materials.

[0581] 4. Generative AI models generate training materials

[0582] The generative AI model analyzes provided procedures and workflows to generate educational presentation materials and training manuals, such as "Points to remember when visiting customers" and "How to use sales report templates."

[0583] 5. The server sends the generated training materials to the device.

[0584] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[0585] 6. User confirms and provides materials

[0586] The user checks the generated training materials on the terminal, and after confirming that there are no problems, provides the materials to the new person in charge.

[0587] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[0588] The processing flow will be explained below.

[0589] Processing steps of the handover document creation system

[0590] Creating handover documents

[0591] Step 1:

[0592] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[0593] Step 2:

[0594] The device sends the entered or uploaded business data to the server, which then packets keywords and documents and sends a request to the server via API.

[0595] Step 3:

[0596] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, the server instructs the AI ​​model to "create handover documents" and provides the data to the AI ​​model.

[0597] Step 4:

[0598] The generative AI model automatically generates conversation content and materials for handovers based on business data. The AI ​​model generates a conversation scenario based on the business content and converts it into a Markdown document to create the materials.

[0599] Step 5:

[0600] The server receives the generated handover material and transmits it to the user's terminal. The server repackets the material and transmits it to the user's terminal.

[0601] Step 6:

[0602] The user checks the handover documents generated on the device, edits them as necessary, and shares them with subordinates or successors.

[0603] FAQ creation

[0604] Step 1:

[0605] The user uses a device to upload past email data and chat history related to the work to be handed over. For example, the user uploads customer support email history from the past year.

[0606] Step 2:

[0607] The device sends the uploaded email and chat history data to the server, where it analyzes the data and converts it into a data format suitable for sending to the server.

[0608] Step 3:

[0609] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the data to the AI ​​model.

[0610] Step 4:

[0611] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers. The AI ​​analyzes data patterns and generates FAQs such as "About the product warranty period" and "How to process a refund."

[0612] Step 5:

[0613] The server sends the generated FAQ to the user's terminal. The server repackets the FAQ and sends it to the user's terminal.

[0614] Step 6:

[0615] The user reviews the generated FAQ on the device, corrects or adds answers as needed, and provides it to the new support representative.

[0616] Training Support

[0617] Step 1:

[0618] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0619] Step 2:

[0620] The terminal sends the entered or uploaded procedure manual and business flow data to the server, which then packets the data and sends it to the server.

[0621] Step 3:

[0622] The server provides the received business data to the generative AI model and instructs it to generate training materials. Specifically, the server instructs the AI ​​model to "create training materials" and provides the data to the AI ​​model.

[0623] Step 4:

[0624] The generative AI model analyzes the provided procedure manuals and business flows to generate educational presentation materials and training manuals. The AI ​​analyzes the procedure manuals and business flows to generate materials such as "key points to remember when visiting customers" and "how to use sales report templates."

[0625] Step 5:

[0626] The server receives the generated training materials and sends them to the user's terminal. The server repackets the materials and sends them to the user's terminal.

[0627] Step 6:

[0628] The user checks the training materials generated on the terminal, and after checking the contents as necessary, provides the materials to the new personnel.

[0629] Through the above process, the efficiency of business handover and rapid business adaptation can be achieved.

[0630] Example 1

[0631] 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."

[0632] Currently, many companies face inefficient manual processes for handing over tasks, creating FAQs, and preparing training materials. It's also difficult to maintain the accuracy and consistency of information, making it difficult to quickly hand over tasks to new staff. This leads to reduced operational efficiency and the risk of information leaks.

[0633] 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.

[0634] In this invention, the server includes means for receiving business data entered by a user, means for transmitting the received business data to the server, means for the server to instruct the generation AI model to analyze and generate materials, means for the generation AI model to generate handover materials based on the analysis results, means for transmitting the generated handover materials from the server to the user's terminal, and means for the user to check and edit the generated materials on the terminal. This enables efficient business handover, rapid creation of FAQs, and automatic generation of training materials, enabling rapid handover to new personnel while maintaining information accuracy and consistency.

[0635] "Business Data" means information entered or uploaded by a User in the course of performing a Business, including text, documents, images, and other file formats.

[0636] A "terminal" is an electronic device operated by a user to input or upload business data, and includes a personal computer, tablet, smartphone, etc.

[0637] A "server" is a central processing unit that analyzes received business data and instructs the AI ​​model to generate materials, and has the ability to communicate with multiple terminals via a network.

[0638] A "generative AI model" is an artificial intelligence model that automatically generates handover documents, FAQs, and training materials based on input data, and analyzes data using natural language processing and machine learning techniques.

[0639] "Handover materials" are documents created by a generative AI model for the purpose of business handover, saved in Markdown format or similar, and containing an overview of the business and detailed procedures.

[0640] An "FAQ" is a collection of frequently asked questions and their answers about specific tasks or products, and is automatically generated by a generative AI model that analyzes past email and chat history.

[0641] "Training materials" are learning materials created by a generative AI model based on work procedures and workflows, and are intended to support the education of new employees and newly appointed personnel.

[0642] "Upload" refers to the act of a user sending data to a server using a terminal, and includes the operation of selecting data through a file selection or input form and pressing the send button.

[0643] "Packetization" refers to the process of dividing the data to be transmitted into a series of data packets and converting them into a format that can be efficiently transmitted over a network.

[0644] "API" is an abbreviation for Application Programming Interface, which enables data communication between servers and terminals, and sends and receives data using standardized communication protocols and data formats.

[0645] "Analysis" refers to the process by which the server or generative AI model analyzes the data it receives and extracts the necessary information, including operations such as formatting, classifying, and summarizing the data.

[0646] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[0647] System configuration

[0648] The system consists of the following major hardware and software components:

[0649] Terminal: A device such as a personal computer, tablet, or smartphone that allows a user to input and upload data.

[0650] Server: A central processing unit that analyzes data and instructs the generative AI model to generate materials.

[0651] Generative AI model: An artificial intelligence model that automatically generates handover materials, FAQs, and training materials based on business data.

[0652] Network: The infrastructure for data communication between devices and servers.

[0653] An embodiment of a handover document creation system

[0654] 1. User enters and uploads business data:

[0655] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user can input keywords such as "marketing strategy" or "advertising cost calculation" and upload related PDF files.

[0656] 2. The device sends the data to the server:

[0657] The terminal converts the data entered or uploaded by the user into the appropriate format and sends it to the server, where it is packetized and transmitted using the HTTPS protocol.

[0658] 3. The server instructs the generative AI model:

[0659] The server analyzes the received business data and instructs the AI ​​model to generate handover documents. The specific prompt is something like "Create handover documents."

[0660] 4. The generative AI model generates handover materials:

[0661] The generative AI model analyzes the provided data and automatically generates handover documents, which are saved in Markdown format.

[0662] 5. The server sends the generated data to the device:

[0663] The server compiles the handover materials received from the generative AI model and sends them to the user's device, where the user can view the generated materials.

[0664] 6. User reviews and edits the document:

[0665] The user can check the generated handover documents on the device and edit them as necessary. The device is provided with a Markdown editor and preview function.

[0666] FAQ creation system embodiment

[0667] 1. User uploads past email and chat history:

[0668] Users can use their devices to upload past email and chat history, for example, uploading customer support email history.

[0669] 2. The device sends the history data to the server:

[0670] The device converts the uploaded data into an appropriate format and sends it to the server.

[0671] 3. The server instructs the generative AI model to generate the FAQ:

[0672] The server analyzes the received data and instructs the generative AI model to generate an FAQ, using prompts such as "Generate an FAQ."

[0673] 4. The generative AI model generates the FAQ:

[0674] The generative AI model analyzes historical data and automatically generates FAQs and their answers, such as questions like "What is the product warranty period?"

[0675] 5. The server sends the generated FAQ to the device:

[0676] The server compiles the generated FAQs and sends them to the user's terminal.

[0677] 6. User checks and edits FAQ:

[0678] The user checks the generated FAQ on the terminal and makes corrections as necessary.

[0679] Embodiment of training support system

[0680] 1. User uploads a procedure or workflow:

[0681] Users use the terminal to input or upload work procedures and workflow diagrams. For example, they can upload procedures such as "procedures for visiting customers" and "how to write sales reports."

[0682] 2. The device sends business data to the server:

[0683] The uploaded data is sent from the terminal to the server.

[0684] 3. The server instructs the generative AI model to generate training materials:

[0685] The server analyzes the received data and instructs the generative AI model to generate training materials.

[0686] 4. The generative AI model generates training materials:

[0687] The generative AI model analyzes procedures and workflows to automatically generate training materials, such as "key points to remember when visiting customers."

[0688] 5. The server sends the generated training materials to the device:

[0689] The server compiles the generated training materials and sends them to the user's terminal.

[0690] 6. User confirms and provides materials:

[0691] The user checks the training materials generated on the terminal, verifies that there are no problems, and then provides them to the new person in charge.

[0692] Example prompt

[0693] 1. When creating handover documents:

[0694] Please compile past business data related to "marketing strategies" and "advertising cost calculations" and prepare handover materials.

[0695] 2. For creating FAQs:

[0696] Generate a list of frequently asked questions and answers based on the past year's worth of customer support emails.

[0697] 3. For training materials:

[0698] Create training materials based on the procedures for "Customer Visit Procedures" and "How to Write a Sales Report."

[0699] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[0700] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0701] Processing steps of the handover document creation system

[0702] Step 1:

[0703] Users use their devices to input or upload business data keywords and related documents. Specifically, they enter keywords such as "marketing strategy" or "advertising cost calculation" into a text box through the device's web interface, and select the related PDF file from the file upload dialog. The input here is the keywords and PDF file, and the output is the data stored on the device.

[0704] Step 2:

[0705] The terminal packets business data entered by the user and sends it to the server. Specifically, a script running on the terminal reads the entered text data or uploaded files and converts it into an appropriate format, such as JSON. The data is then sent to the server via an API using HTTPS. The input here is JSON-formatted data, and the output is that data being sent to the server.

[0706] Step 3:

[0707] The server analyzes the received business data and instructs the generative AI model to create handover documents. Specifically, the server formats and validates the data, checking for inappropriate data. After analyzing the data, it sends a request to the generative AI model along with a prompt such as "Create handover documents." The input is the processed business data and the prompt, and the output is a request sent to the generative AI model.

[0708] Step 4:

[0709] The generative AI model generates handover materials based on the received data. Specifically, the generative AI model analyzes business data, extracts relevant information, and compiles it into a Markdown-formatted document. For example, the "Marketing Strategy" section contains content based on past cases and successful strategies. The input is business data and prompt text, and the output is the generated Markdown-formatted handover materials.

[0710] Step 5:

[0711] The server sends the handover materials received from the generative AI model to the user's device. Specifically, the generated materials are linked to the user ID and session information, stored in a temporary file, and notified to the user's device via a notification API. The notification includes a download link for the materials. The input is the generated materials, and the output is a notification sent to the user's device.

[0712] Step 6:

[0713] The user checks and edits the handover materials generated on the device. Specifically, the user checks the content using the Markdown editor and preview function displayed on the device and makes corrections as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated material, and the output is the corrected final material.

[0714] FAQ creation system processing steps

[0715] Step 1:

[0716] The user uploads past email and chat history using the device. Specifically, the user selects and uploads the past year's worth of email history from the "Customer Support" folder through the device's file upload dialog. The input is an email history file, and the output is the history data stored on the device.

[0717] Step 2:

[0718] The terminal packetizes the uploaded history data and sends it to the server. Specifically, a script on the terminal parses the history data and converts it into an appropriate format such as CSV or JSON. It then sends it to the server via API using the HTTPS protocol. The input is the converted history data, and the output is the data sent to the server.

[0719] Step 3:

[0720] The server analyzes the received history data and instructs the generative AI model to generate FAQs. Specifically, the server organizes the history data by theme and sends a request to the generative AI model along with a prompt such as "Generate an FAQ." The input is the organized history data and the prompt, and the output is a request sent to the generative AI model.

[0721] Step 4:

[0722] The generative AI model generates FAQs based on historical data. Specifically, the generative AI model analyzes historical data and automatically generates frequently asked questions and their answers. For example, a question and answer about the "warranty period of a product" is created. The input is historical data and prompt text, and the output is the generated FAQ.

[0723] Step 5:

[0724] The server sends the generated FAQ to the user's device. The generated FAQ is stored as a temporary file, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated FAQ, and the output is the notification sent to the user's device.

[0725] Step 6:

[0726] The user checks and edits the generated FAQ on the device. Specifically, the user checks the questions and answers in the FAQ editing interface displayed on the device, and makes corrections or additions as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated FAQ, and the output is the final, modified FAQ.

[0727] Training support system processing steps

[0728] Step 1:

[0729] Users use their devices to upload work procedures and workflows. Specifically, they select and upload procedures such as "Procedures for visiting customers" or "How to write sales reports" from the device's file upload dialog. The input is the procedure file, and the output is data stored on the device.

[0730] Step 2:

[0731] The terminal packetizes the uploaded business data and sends it to the server. Specifically, a script on the terminal analyzes the business data, converts it into an appropriate format, and sends it to the server using the HTTPS protocol. The input is the converted business data, and the output is the data sent to the server.

[0732] Step 3:

[0733] The server analyzes the received business data and instructs the generative AI model to generate training materials. Specifically, the server analyzes the procedure manual and business flow and sends a request to the generative AI model along with a prompt. The input is the procedure manual data and the prompt, and the output is a request sent to the generative AI model.

[0734] Step 4:

[0735] The generative AI model generates training materials based on the data it receives. Specifically, the generative AI model analyzes procedures and business flows, and automatically generates educational presentation materials and training manuals. For example, it creates materials such as "Points to remember when visiting customers" and "How to use sales report templates." The input is business data and prompts, and the output is the generated training materials.

[0736] Step 5:

[0737] The server sends the generated training materials to the user's device. The generated materials are stored as temporary files, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated training materials, and the output is a notification of the sending to the user's device.

[0738] Step 6:

[0739] The user checks and provides the generated training materials on the terminal. Specifically, the user checks the training materials on the terminal, confirms that there are no problems, and then prepares to provide them to the new person in charge. The user reviews the materials and checks for errors or omissions. The input is clicking a link in the generated training materials, and the output is the final, confirmed training materials.

[0740] (Application example 1)

[0741] 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."

[0742] In modern manufacturing, rapid training and efficient implementation of new work procedures and workflows are key challenges. In particular, in-factory worker training often relies on manual explanations and training, which requires time and effort. Furthermore, there is a lack of technological means to efficiently manage work-related data and automatically generate handover and training materials based on that data. This creates a need for rapid and accurate training of new work procedures and workflows.

[0743] 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.

[0744] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and executing a generative model for automatically generating handover materials, means for outputting the generated handover materials, means for a factory worker to input or upload work procedures and workflows to a robot, means for the robot to send data to a central server, means for the central server to instruct the generative AI model to generate training materials, means for the generative AI model to generate training materials, means for the central server to send the generated training materials to the robot, and means for the robot to display the materials and for the worker to review and use. This enables factory workers to efficiently receive training on new work procedures and workflows.

[0745] "Business Data" refers to business-related information, documents, procedures, or business flows that are entered or uploaded by a user.

[0746] A "generative model" refers to an algorithm or machine learning model that analyzes data entered by a user and automatically generates materials and answers.

[0747] A "generative AI model" refers to an advanced model that uses artificial intelligence to analyze data entered by users and automatically generate materials.

[0748] "Handover materials" refers to documents that compile the information and procedures necessary to hand over work to another person in charge.

[0749] "Central Server" refers to a central computing device for remote data analysis and management.

[0750] "Training Materials" means educational documents and materials used to learn new business procedures and workflows.

[0751] A "business procedure manual" is a document that contains specific procedures and instructions for carrying out a specific task.

[0752] "Business flow" refers to a flowchart or diagram that shows the series of processes and steps by which a specific business is carried out.

[0753] A "robot" is an automated machine used in factories that has the ability to analyze data entered by workers and display materials.

[0754] A "display screen" refers to a display device installed on a robot or terminal, which visually displays generated materials and information.

[0755] "Factory workers" refer to employees who are engaged in tasks such as machine operation and assembly work within a factory.

[0756] This invention is a system that supports efficient training and job handover for factory workers. Specifically, the system allows users to provide work procedures and workflows, and a central server automatically generates training materials using a generative AI model based on these. The materials are then displayed by a robot.

[0757] Program processing explanation

[0758] Hardware and software used

[0759] Robot terminal: Robots used in factories are equipped with a display screen that provides an interface for workers to input work procedures and workflows.

[0760] Central server: A computing device for data management and processing that processes received business data and instructs the generative AI model on how to analyze it. Specific software includes a RESTful API for transferring data via API.

[0761] Generative AI models: Use advanced artificial intelligence models, such as OpenAI's GPT-4, to analyze the information you provide and automatically generate training and handover materials.

[0762] Specific program behavior

[0763] 1. Data entry: Factory workers use robotic terminals to input or upload new work procedures and workflows, such as "new assembly line procedures" or "machine maintenance workflows."

[0764] 2. Data transmission: The robot terminal transmits the entered or uploaded data to the central server. The data is transmitted using an HTTP POST request.

[0765] 3. Data analysis and material generation: The central server provides the received data to the generative AI model and instructs it to generate training materials. The generative AI model automatically generates training materials based on the data provided by the user.

[0766] 4. Material transmission and display: The generated training materials are sent back to the robot terminal by the central server, and the robot terminal displays the materials on the display screen for factory workers to review and use.

[0767] Adding specific examples

[0768] Prompt Sentence Examples

[0769] Please generate training materials based on the following operating procedures:

[0770] 1. Assembly line preparation

[0771] 2. Safety Check

[0772] 3. Machine start-up procedure

[0773] 4. Production line operation method

[0774] …

[0775] By providing the above prompts to the generative AI model, the model can analyze the user's work procedures and automatically generate relevant training materials, allowing factory workers to quickly master the new work procedures.

[0776] As described above, the present invention is a system that utilizes a generative AI model based on business data provided by the user to automatically generate and efficiently display business handover and training materials, thereby improving business efficiency and enabling rapid adaptation to new business operations.

[0777] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0778] Step 1:

[0779] Users use the robot terminal to input or upload work procedures and workflows. The input data includes specific details of the procedures and workflows, such as "new assembly line procedures" or "machine maintenance flow," in text or PDF format.

[0780] Step 2:

[0781] The robot terminal sends the input or uploaded business data to the central server. To do this, the robot terminal uses an HTTP POST request to send the data to the server via an API. The input data is packetized, converted into the appropriate format, and then sent.

[0782] Step 3:

[0783] The server provides the received data to the generative AI model and instructs it to generate training materials. The server sends the data to the generative AI model using an HTTP request, including a prompt such as "Please generate training materials based on the following business procedure manual."

[0784] Step 4:

[0785] The generative AI model analyzes the provided data and automatically generates training materials. The generative AI model creates specific training materials and manuals based on the work procedures entered by the user. For example, it generates materials including content such as "assembly line preparation," "safety checks," and "machine startup procedures."

[0786] Step 5:

[0787] The server sends the generated training materials to the robot terminal, converts the materials received from the generative AI model into an appropriate format, and sends them back to the robot terminal using an HTTP POST request.

[0788] Step 6:

[0789] The robot terminal displays the generated training materials on the display screen for the user to review and use. The robot terminal then displays the received materials on the display screen, allowing factory workers to review work procedures based on the materials and apply them to their actual work.

[0790] 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.

[0791] This invention analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine.By using the emotion engine, it is possible to analyze user emotions and reflect them in the generated materials.

[0792] An embodiment of a handover document creation system

[0793] Program processing explanation

[0794] 1. The user enters business data

[0795] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[0796] 2. Emotion engine recognizes emotions

[0797] The emotion engine recognizes the emotion of the user as they type. For example, the emotion engine analyzes the emotions (joy, anger, sadness, etc.) that the user is expressing while typing text.

[0798] 3. The device sends the data to the server

[0799] The device sends the input business data and the recognized emotion data to the server, packetizes the data, and sends a request to the server via API.

[0800] 4. The server instructs the generative AI model

[0801] The server analyzes the received business data and emotional data and issues instructions to the AI ​​model to generate documents. Specifically, it instructs the AI ​​model to "create handover documents," and also provides the emotional data to the AI ​​model.

[0802] 5. Generative AI model generates handover documents

[0803] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotional data. The AI ​​model adjusts the conversation scenario to reflect the emotional data, and converts it into a Markdown-formatted document to create the materials.

[0804] 6. The server sends the generated data to the terminal.

[0805] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[0806] 7. The user checks and edits the document

[0807] The user checks the handover materials generated on the terminal and edits and uses them as necessary, for example, by adding additional information to the materials or adjusting the layout.

[0808] FAQ creation system embodiment

[0809] 1. Users upload their past email and chat history

[0810] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[0811] 2. Emotion engine recognizes emotions

[0812] The emotion engine recognizes user emotions from email and chat history, for example, extracting emotions (e.g., customer dissatisfaction or satisfaction) within each email or chat.

[0813] 3. The device sends the history data to the server

[0814] Uploaded email and chat history data and recognized emotion data are sent from the device to the server, where the data is analyzed and converted into a data format suitable for sending to the server.

[0815] 4. The server instructs the generative AI model to generate FAQs

[0816] The server provides the received history data and emotion data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the emotion data to the model.

[0817] 5. Generative AI model generates FAQs

[0818] The generative AI model analyzes the provided historical and sentiment data to automatically generate frequently asked questions and their answers. For example, based on customer sentiment analysis, it generates FAQs such as "About the product warranty period" and "How to process a refund."

[0819] 6. The server sends the generated FAQ to the device.

[0820] The server sends the generated FAQ and its answers to the user's terminal, where the data is repacketized and sent to the user's terminal.

[0821] 7. Users check and edit the FAQ

[0822] The user checks the FAQ generated on the terminal, makes corrections or additions as necessary, and provides it to the new support staff.

[0823] Embodiment of training support system

[0824] 1. The user inputs or uploads a business procedure manual or workflow.

[0825] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[0826] 2. Emotion engine recognizes emotions

[0827] The emotion engine recognizes the user's emotions in the procedure manual or workflow. For example, it analyzes the emotions (satisfaction, anxiety, etc.) when creating the procedure manual.

[0828] 3. The device sends business data to the server

[0829] The uploaded business data and recognized emotion data are sent from the device to the server, which then packets the data and sends it to the server.

[0830] 4. The server instructs the AI ​​model to generate training materials.

[0831] The server provides the received business data and emotional data to the generative AI model and instructs it to generate training materials. Specifically, it instructs the AI ​​model to "create training materials" and provides the emotional data to the model.

[0832] 5. Generative AI models generate training materials

[0833] The generative AI model analyzes business data and emotional data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[0834] 6. The server sends the generated training materials to the device.

[0835] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[0836] 7. User confirms and provides materials

[0837] The user checks the training materials generated on the terminal, checks the contents as necessary, and then provides the materials to the new person in charge.

[0838] As described above, this invention utilizes a generative AI model and an emotion engine to automatically generate materials based on data and emotion data entered by the user, and provides the results to the user, thereby streamlining business handover and enabling rapid adaptation to new business operations.

[0839] The processing flow will be explained below.

[0840] Processing steps of the handover document creation system

[0841] Creating handover documents

[0842] Step 1:

[0843] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[0844] Step 2:

[0845] The emotion engine recognizes the user's emotions. The emotion engine analyzes emotions such as "stress" and "expectation" from the text entered by the user and the contents of related documents.

[0846] Step 3:

[0847] The device sends the input business data and emotion data to the server. Specifically, the encoded data is packetized and sent to the server via an API.

[0848] Step 4:

[0849] The server provides the received business data and emotion data to the generative AI model and instructs it to generate documents. The server then sends the data to the AI ​​model along with the instruction to "create handover documents."

[0850] Step 5:

[0851] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotion data. The materials are created in Markdown format and include explanations and points of caution that reflect the emotion data.

[0852] Step 6:

[0853] The server sends the generated handover materials to the terminal. The server compiles the materials, packets them into a format that is easy for the user to understand, and sends them to the terminal.

[0854] Step 7:

[0855] The user checks the generated handover documents on the terminal and edits them as necessary, for example, by correcting the text of the documents or adding additional information.

[0856] FAQ creation

[0857] Step 1:

[0858] The user uses a device to upload past email data and chat history related to the work being handed over. For example, upload the past year's worth of customer support email history.

[0859] Step 2:

[0860] The emotion engine recognizes user emotions from email and chat history, extracting emotions such as "satisfied" or "dissatisfied" from customer interactions.

[0861] Step 3:

[0862] The device sends the uploaded history data and emotion data to the server, which analyzes the encoded data, converts it into a format suitable for transmission, and sends it to the server.

[0863] Step 4:

[0864] The server instructs the generation AI model to generate an FAQ. The server instructs the AI ​​model to "create an FAQ" and provides historical data and emotional data to the AI ​​model.

[0865] Step 5:

[0866] Generative AI models analyze historical and sentiment data to automatically generate frequently asked questions (FAQs) and their answers, for example, by taking customer sentiment into account.

[0867] Step 6:

[0868] The server sends the generated FAQ to the terminal. The server compiles the information, packets it into an appropriate format, and sends it to the user's terminal.

[0869] Step 7:

[0870] The user checks the generated FAQ on the device and makes corrections or additions as necessary, for example, adding new questions or making changes to clarify the answers.

[0871] Training Support

[0872] Step 1:

[0873] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, they can upload procedure manuals such as "procedures for visiting customers" or "how to write sales reports."

[0874] Step 2:

[0875] The emotion engine recognizes the user's emotions from the procedure manual and business flow, analyzing emotions such as "sense of relief" and "confusion" from the contents and explanations of the procedure manual.

[0876] Step 3:

[0877] The device sends business data and emotion data to the server, which encodes the data into packets and sends them to the server via API.

[0878] Step 4:

[0879] The server instructs the AI ​​model to generate training materials. The server instructs the AI ​​model to "create training materials" and provides data and emotional information to the AI ​​model.

[0880] Step 5:

[0881] The generative AI model analyzes business data and emotion data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[0882] Step 6:

[0883] The server sends the generated training materials to the terminal, where it compiles the materials, packets them into an appropriate format for delivery to the user, and transmits them.

[0884] Step 7:

[0885] The user checks the training materials generated on the terminal, and after reviewing the content as necessary, provides the materials to the new personnel, for example by editing the materials to add additional explanations.

[0886] Through this process, handover materials, FAQs, and training materials that reflect the user's feelings are automatically generated. This system makes the handover process more efficient and enables rapid business adaptation.

[0887] Example 2

[0888] 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."

[0889] In today's business environment, the creation of efficient handover documents, frequently asked questions (FAQs), and training materials is a key challenge. However, creating these documents manually requires a significant amount of time and effort, and often loses emotional nuance and context. It is also difficult to effectively utilize past communications and operating procedures. Therefore, there is a need for automated, efficient, and emotionally sensitive document generation.

[0890] 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.

[0891] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and recognizing emotions, means for transmitting the input data and emotional data from the user terminal to the server, means for automatically generating handover materials, FAQs, and training materials using a generative AI model based on the received data and emotional data, and means for transmitting and outputting the generated materials to the user terminal. This improves the efficiency of automatic generation of work-related materials and enables the creation of more appropriate materials that take user emotions into consideration.

[0892] "Data related to business" refers to information related to business input by a user, and includes all data necessary for business execution, including text, documents, keywords, and the like.

[0893] An "emotion engine" is an algorithm that analyzes text and data entered by users and recognizes and classifies emotions.

[0894] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning techniques to automatically generate specific materials from input data.

[0895] "Handover materials" are documents and guides created to efficiently and accurately hand over work to another person.

[0896] "Frequently Asked Questions (FAQ)" refers to a collection of frequently asked questions and answers from users regarding a particular business or service.

[0897] "Training materials" are educational materials used to enable new personnel and staff to carry out their duties smoothly, and include procedures and workflow diagrams.

[0898] A "server" is a device that receives, analyzes, stores, and transmits data over a network, and in this invention also executes the generative AI model.

[0899] "Terminal" refers to a device that allows a user to input, send, and receive business data and history data, and display the generated materials.

[0900] "Receiving means" refers to a function or process for receiving data entered by a user.

[0901] "Transmission means" refers to a function or process for transmitting received data or generated materials to a server or user terminal.

[0902] "Output means" refers to the functions and processes for providing the generated materials in a format that can be displayed and edited on the user's terminal.

[0903] "Interface" refers to the operation screen or input means that allows users to input business data and check and edit generated documents.

[0904] This invention is a system that uses business data and past communication history entered by users to automatically generate handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine. The implementation method is described in detail below.

[0905] System Configuration

[0906] This system consists of a terminal operated by the user, a server that analyzes and processes data, a generative AI model, and an emotion engine. The terminal is a device into which the user inputs business data, past emails, chat history, etc., and includes PCs and tablets. The server is a central processing unit that analyzes the data and runs the generative AI model. The emotion engine is software that analyzes emotions from user input data and uses NLP (Natural Language Processing) algorithms.

[0907] Generate handover documents

[0908] 1. Input: The user uses the terminal to input business data, either manually using text fields or by uploading relevant documents using drag-and-drop. For example, they can upload keywords such as "marketing strategy" and "advertising cost calculation" and relevant documents.

[0909] 2. Sentiment analysis: The emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) from the input text in real time. This allows not only the text itself but also the user's emotions to be obtained as data.

[0910] 3. Data transmission: The device packets the input business data and recognized emotion data and sends them to the server via API. For example, the data is sent in JSON format using an HTTP request.

[0911] 4. Document generation: The server issues instructions to the AI ​​model to generate documents based on the received data. The prompt text includes specific instructions and emotional data. For example, it might say, "Create handover documents, taking into account the emotional data."

[0912] 5. Output: The generative AI model generates handover materials, converts them into Markdown documents, and sends them back to the server. The server then compiles them and sends them back to the device. The user can review these documents and edit them as needed.

[0913] FAQ generation

[0914] 1. Input: The user uploads past email and chat history from their device. For example, they upload customer support email history from the past year.

[0915] 2. Sentiment Analysis: The sentiment engine analyzes the content of emails and chats to identify the sentiment within each message.

[0916] 3. Data transmission: The device transmits history data and emotion data to the server.

[0917] 4. FAQ generation: The server instructs the AI ​​model to generate FAQs. For example, it uses a prompt such as "Generate frequently asked questions and their answers from past emails."

[0918] 5. Output: The generative AI model creates FAQs, which are then compiled by the server and sent to the user's device. The user can then review the generated FAQs and edit them as necessary.

[0919] Generate training materials

[0920] 1. Input: The user inputs the work procedure manual or workflow diagram from the terminal or uploads it in PDF format. For example, they can upload "procedures for visiting customers" or "how to write sales reports."

[0921] 2. Sentiment Analysis: The emotion engine analyzes emotions in procedures and flow diagrams. For example, it analyzes "anxiety" from the text in a procedure manual.

[0922] 3. Data transmission: The device transmits the business data and the recognized emotion data to the server.

[0923] 4. Material generation: The server instructs the AI ​​model to generate training materials, for example, by using a prompt such as "Create training materials."

[0924] 5. Output: The generative AI model generates training materials, which are then compiled by the server and sent to the user's device. The user can review the materials and edit them as necessary before providing them to the new person in charge.

[0925] Examples of specific examples and prompts

[0926] As a concrete example, consider the case where a user is creating handover documents for a "marketing strategy." When the user uploads the relevant documents, the emotion engine recognizes "joy," and the server sends a prompt to the generative AI model instructing it to "create handover documents, taking into account the emotion data." The generated documents are returned to the terminal in Markdown format, where the user can review and edit them.

[0927] Example prompt sentence:

[0928] "Create a handover document for your marketing strategy. Consider sentiment data."

[0929] In this way, the present invention is a system that enables efficient creation of materials that take emotions into consideration based on business data and emotion data.

[0930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The user enters business data

[0933] The user uses the device to input keywords and related documents related to the business to be handed over or upload them by drag and drop. Specifically, the user may input keywords such as "marketing strategy" or "calculating advertising costs" into the text field on the device, or upload a PDF file of a marketing report. The input of this action is business data, and the output is business data saved on the device.

[0934] Step 2:

[0935] Emotion engine recognizes emotions

[0936] The device provides input business data to the emotion engine, which analyzes the emotions in the text. The emotion engine uses NLP algorithms to extract emotions such as "joy," "anger," and "sadness" from the input text. For example, if a user types "This project is great," the emotion engine recognizes "joy." The input for this operation is business data, and the output is the recognized emotion data.

[0937] Step 3:

[0938] The device sends the data to the server

[0939] The device packets the business data and the recognized emotion data and sends them to the server using an HTTP request. The data is packetized in JSON format and sent to the server's API endpoint. For example, the following JSON format data is sent:

[0940] json

[0941] {

[0942] "Business Data": "Marketing Strategy",

[0943] "Emotion Data": "Joy"

[0944] }

[0945] The inputs for this operation are task data and emotion data, and the output is the data sent to the server.

[0946] Step 4:

[0947] The server instructs the generative AI model

[0948] The server analyzes the received business data and emotion data and issues instructions to the generative AI model to generate materials. Specifically, it generates a prompt sentence and instructs the generative AI model to "create handover materials, taking into account the emotion data." For example, the generated prompt sentence is as follows:

[0949] "Create a handover document for your marketing strategy. Consider sentiment data."

[0950] The inputs for this behavior are incoming data and emotion data, and the output is instructions to the generative AI model.

[0951] Step 5:

[0952] Generative AI model generates handover documents

[0953] The generative AI model automatically generates handover materials based on the provided business data and emotion data. The generative AI model uses deep learning to analyze the input data and generate handover materials in Markdown format. For example, a document containing detailed explanations and procedures for a "marketing strategy" is generated. The input for this operation is a prompt and data, and the output is the generated handover materials.

[0954] Step 6:

[0955] The server sends the generated data to the terminal.

[0956] The server collects the handover data received from the generative AI model, repackets it, and sends it to the user's device. The data is then repacketized in an HTTP request and sent to the user's device. For example, the following Markdown-formatted data is sent:

[0957] markdown

[0958] Marketing Strategy

[0959] This article will introduce you to the key points of your marketing strategy...

[0960] The input of this operation is the generated handover material, and the output is the material sent to the user terminal.

[0961] Step 7:

[0962] Users can view and edit documents

[0963] The user reviews the handover materials generated on their device and makes edits as needed. They use document editing software or a web application to add content to the materials or adjust their layout. For example, they may add specific figures or graphs related to a marketing strategy. The input for this operation is the received handover materials, and the output is the edited final materials.

[0964] (Application example 2)

[0965] 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."

[0966] In today's business environment, efficient work handovers and improved customer support quality are required. However, traditional manual handover materials and FAQ generation require a significant amount of time and effort, and are difficult to consider individual emotions. Furthermore, preparing training materials is also a significant burden, making it difficult for new employees to quickly adapt to their work. To solve these problems, a system is needed that automatically analyzes information such as business data and customer interaction history, and generates optimal materials based on emotional data.

[0967] 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.

[0968] In this invention, the server includes a means for receiving data related to work input by a user, a means for analyzing emotions based on the received data, a means for analyzing the data and emotions and executing a generative model for automatically generating handover materials, and a means for outputting the generated handover materials. This makes it possible to create handover materials efficiently and with consideration for emotions based on the work data and emotion data.

[0969] The server also includes a means for receiving past communication history input by the user, a means for analyzing emotions from the received history data, a means for executing a generative model for automatically generating frequently asked questions and their answers based on the analyzed emotions, and a means for outputting the generated frequently asked questions and their answers. This makes it possible to generate optimal FAQs based on past customer interaction history and analyzed emotion data.

[0970] The server further includes a means for receiving a work procedure manual or work flow input by a user, a means for analyzing emotions from the received data, a means for executing a generative model for automatically generating training materials based on the analyzed data and emotions, and a means for outputting the generated training materials, thereby enabling efficient creation of training materials that enable new employees to quickly adapt to their work.

[0971] "Business data" is a general term for information, documents, instructions, procedures, etc. used by users when performing their business.

[0972] "Means for analyzing emotions" refers to a system that extracts emotional elements from user input data and history data, and classifies, evaluates, and analyzes the type and intensity of emotions.

[0973] A "generative model for generating handover materials" is a model used to automatically generate the materials necessary for the next worker to take over the work, based on the received work data and emotion data.

[0974] "Means for outputting generated handover materials" refers to means for providing the handover materials created by the generative model to the user, and refers to outputting them as electronic data or printed materials.

[0975] "Communication history" is a general term for records of past communications such as emails and chats, and includes messages and related information exchanged between a user and other people.

[0976] A "generative model for generating frequently asked questions and their answers" is a model used to automatically generate FAQs (frequently asked questions) and their answers based on communication history and emotional data.

[0977] "Means for outputting the generated frequently asked questions and their answers" refers to the means for providing the FAQs created by the generative model and their answers to users, and refers to outputting them as electronic data or printed material.

[0978] A "work procedure manual" is a document that describes the specific procedures for carrying out a specific task.

[0979] A "business flow" is a diagram or chart that visually represents the flow or process of a business.

[0980] A "generative model for generating training materials" is a model used to automatically generate materials necessary for training new employees, etc., based on business data and emotion data.

[0981] "Means for outputting generated training materials" refers to means for providing training materials created by a generative model to users, and refers to outputting them as electronic data or printed materials.

[0982] MODE FOR CARRYING OUT THE INVENTION

[0983] This invention relates to a business support system that handles business data, past communication history, business procedure manuals, and business flows. Specifically, it is a system that analyzes data entered by users and automatically generates handover documents, FAQs, and training materials using a generative AI model and an emotion engine.

[0984] System Configuration

[0985] The system includes the following main components:

[0986] 1. User terminal: Provides an interface for inputting or uploading business data, past communication history, business procedures, and business flows. For example, devices such as PCs, smartphones, and tablets are used.

[0987] 2. Server: This is the central system that receives data, parses it, analyzes sentiment, runs generative AI models, and distributes the generated materials.

[0988] 3. Emotion engine: Software for analyzing emotions from incoming and historical data. For example, the transformers library and its emotion analysis module are used.

[0989] 4. Generative AI model: An AI model for generating handover materials, FAQs, and training materials based on the received data and sentiment data. For example, OpenAI's text-davinci-003 model is used.

[0990] Program processing description

[0991] The server performs the following process:

[0992] 1. Data reception and preprocessing: The system receives business data, communication history, business procedures, and business flows entered by the user from their terminal. The user terminal converts these into a data format and sends them to the server.

[0993] 2. Sentiment Analysis: The received data is analyzed by the emotion engine to extract the emotional components in the data. For example, the user's input is classified as "positive," "negative," or "neutral."

[0994] 3. Execution of the generative AI model: Based on the results of the sentiment analysis and business data, the generative AI model generates handover materials, FAQs, and training materials. For example, it issues instructions using prompt sentences like the following:

[0995] text

[0996] Context: {Past data and historical information}

[0997] User Input: {User's specific question or request}

[0998] Emotion: {Parsed emotion label}

[0999] Generate a helpful response with appropriate sentiment.

[1000] 4. Creation and distribution of materials: The created materials are sent to the user's terminal via the server again, allowing the user to view, edit, and use them.

[1001] Specific examples

[1002] For example, if a user enters a comment such as "I can't set up the product properly, it's stressful," the system will operate as follows:

[1003] 1. Receive the data entered by the user and assign the emotion label "negative."

[1004] 2. Incorporate emotion labels and input data into prompt sentences.

[1005] text

[1006] Context: Past customer support history

[1007] User Input: I'm having trouble setting up my product. What should I do?

[1008] Emotion: Negative

[1009] Generate a helpful response with appropriate sentiment.

[1010] 3. The generative AI model generates an appropriate response and provides it to the user device via the server.

[1011] In this way, the system can analyze emotions and automatically generate optimal materials based on the user's work data, communication history, work procedures, and workflow. This not only dramatically improves the efficiency of work handovers and customer support, but also automates the creation of training materials to help new employees quickly adapt to their work.

[1012] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1013] Step 1:

[1014] Inputting and receiving data via a user terminal

[1015] The user uses the device to input or upload business data, past communication history, work procedures, or work flows. For example, they can upload data about "product configuration problems," past email and chat history, and work procedures. The device receives this data and converts it into the appropriate data format.

[1016] Step 2:

[1017] Data transmission to server and pre-processing

[1018] The device converts data entered or uploaded by the user into packets and sends them to the server via API. Specifically, it sends converted data including business data, communication history, procedure manuals, etc. The server receives this data and stores it in the appropriate storage.

[1019] Step 3:

[1020] Performing sentiment analysis

[1021] The server passes the received data to the emotion engine for emotion analysis. For example, it uses the transformers library to extract and classify emotion elements (e.g., positive, negative, neutral) from the data. The analysis results in emotion labels and their intensities.

[1022] Step 4:

[1023] Prompt generation for generative AI models

[1024] The server generates a prompt sentence to be passed to the generative AI model based on the emotion analysis results and the received data. As a specific example, the following prompt sentence is created:

[1025] text

[1026] Context: Past customer support history

[1027] User Input: I'm having trouble setting up my product. What should I do?

[1028] Emotion: Negative

[1029] Generate a helpful response with appropriate sentiment.

[1030] Step 5:

[1031] Execution of generative AI models and data generation

[1032] The server uses the generated prompt to run a generative AI model (e.g., OpenAI's text-davinci-003), which generates appropriate handover materials, FAQs, or training materials based on the prompt. The generated results are output in text format.

[1033] Step 6:

[1034] Distribution of generated materials

[1035] The server repackets the output from the generative AI model and sends it to the user's device, where the delivered materials can be viewed, edited, and output.

[1036] Step 7:

[1037] User confirmation and use of materials

[1038] The user reviews the received documents on their device, edits the content or adds additional information as needed, and then prints or digitally provides the documents to other personnel. For example, handover documents are likely to include "Troubleshooting Procedures for Configuration Issues" and answers to frequently asked questions.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] [Third embodiment]

[1043] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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).

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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."

[1055] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[1056] An embodiment of a handover document creation system

[1057] Program processing explanation

[1058] 1. The user enters business data

[1059] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[1060] 2. The device sends the data to the server

[1061] The terminal sends business data entered or uploaded by the user to the server, including the process of packetizing the input data and sending it to the server via API.

[1062] 3. The server instructs the generative AI model

[1063] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, it passes instructions such as "Create handover documents" to the AI ​​model along with the data.

[1064] 4. Generative AI model generates handover documents

[1065] The generative AI model automatically generates conversation content and materials for handovers based on the provided business data. The generated materials are saved in Markdown format.

[1066] 5. The server sends the generated data to the device.

[1067] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[1068] 6. The user checks and edits the document

[1069] The user checks the handover materials generated on the terminal, and edits and uses them as necessary.

[1070] FAQ creation system embodiment

[1071] 1. Users upload their past email and chat history

[1072] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[1073] 2. The device sends the history data to the server

[1074] Uploaded emails and chat histories are sent from the device to the server, where the device analyzes the data and converts it into an appropriate format for transmission to the server.

[1075] 3. The server instructs the AI ​​model to generate FAQs

[1076] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers.

[1077] 4. Generative AI model generates FAQs

[1078] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers, such as "About the product warranty period" and "How to process a refund."

[1079] 5. The server sends the generated FAQ to the device.

[1080] The server sends the generated FAQ to the user's terminal, allowing the user to view the FAQ.

[1081] 6. Users check and edit the FAQ

[1082] The user can check the FAQ generated on the device and make corrections or additions as necessary.

[1083] Embodiment of training support system

[1084] 1. The user inputs or uploads a business procedure manual or workflow.

[1085] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1086] 2. The device sends business data to the server

[1087] The uploaded business data is sent from the terminal to the server, where it is packetized and sent to the server.

[1088] 3. The server instructs the AI ​​model to generate training materials.

[1089] The server provides the received business procedures and workflow to the generative AI model and instructs it to generate training materials.

[1090] 4. Generative AI models generate training materials

[1091] The generative AI model analyzes provided procedures and workflows to generate educational presentation materials and training manuals, such as "Points to remember when visiting customers" and "How to use sales report templates."

[1092] 5. The server sends the generated training materials to the device.

[1093] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[1094] 6. User confirms and provides materials

[1095] The user checks the generated training materials on the terminal, and after confirming that there are no problems, provides the materials to the new person in charge.

[1096] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[1097] The processing flow will be explained below.

[1098] Processing steps of the handover document creation system

[1099] Creating handover documents

[1100] Step 1:

[1101] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[1102] Step 2:

[1103] The device sends the entered or uploaded business data to the server, which then packets keywords and documents and sends a request to the server via API.

[1104] Step 3:

[1105] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, the server instructs the AI ​​model to "create handover documents" and provides the data to the AI ​​model.

[1106] Step 4:

[1107] The generative AI model automatically generates conversation content and materials for handovers based on business data. The AI ​​model generates a conversation scenario based on the business content and converts it into a Markdown document to create the materials.

[1108] Step 5:

[1109] The server receives the generated handover material and transmits it to the user's terminal. The server repackets the material and transmits it to the user's terminal.

[1110] Step 6:

[1111] The user checks the handover documents generated on the device, edits them as necessary, and shares them with subordinates or successors.

[1112] FAQ creation

[1113] Step 1:

[1114] The user uses a device to upload past email data and chat history related to the work to be handed over. For example, the user uploads customer support email history from the past year.

[1115] Step 2:

[1116] The device sends the uploaded email and chat history data to the server, where it analyzes the data and converts it into a data format suitable for sending to the server.

[1117] Step 3:

[1118] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the data to the AI ​​model.

[1119] Step 4:

[1120] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers. The AI ​​analyzes data patterns and generates FAQs such as "About the product warranty period" and "How to process a refund."

[1121] Step 5:

[1122] The server sends the generated FAQ to the user's terminal. The server repackets the FAQ and sends it to the user's terminal.

[1123] Step 6:

[1124] The user reviews the generated FAQ on the device, corrects or adds answers as needed, and provides it to the new support representative.

[1125] Training Support

[1126] Step 1:

[1127] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1128] Step 2:

[1129] The terminal sends the entered or uploaded procedure manual and business flow data to the server, which then packets the data and sends it to the server.

[1130] Step 3:

[1131] The server provides the received business data to the generative AI model and instructs it to generate training materials. Specifically, the server instructs the AI ​​model to "create training materials" and provides the data to the AI ​​model.

[1132] Step 4:

[1133] The generative AI model analyzes the provided procedure manuals and business flows to generate educational presentation materials and training manuals. The AI ​​analyzes the procedure manuals and business flows to generate materials such as "key points to remember when visiting customers" and "how to use sales report templates."

[1134] Step 5:

[1135] The server receives the generated training materials and sends them to the user's terminal. The server repackets the materials and sends them to the user's terminal.

[1136] Step 6:

[1137] The user checks the training materials generated on the terminal, and after checking the contents as necessary, provides the materials to the new personnel.

[1138] Through the above process, the efficiency of business handover and rapid business adaptation can be achieved.

[1139] Example 1

[1140] 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."

[1141] Currently, many companies face inefficient manual processes for handing over tasks, creating FAQs, and preparing training materials. It's also difficult to maintain the accuracy and consistency of information, making it difficult to quickly hand over tasks to new staff. This leads to reduced operational efficiency and the risk of information leaks.

[1142] 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.

[1143] In this invention, the server includes means for receiving business data entered by a user, means for transmitting the received business data to the server, means for the server to instruct the generation AI model to analyze and generate materials, means for the generation AI model to generate handover materials based on the analysis results, means for transmitting the generated handover materials from the server to the user's terminal, and means for the user to check and edit the generated materials on the terminal. This enables efficient business handover, rapid creation of FAQs, and automatic generation of training materials, enabling rapid handover to new personnel while maintaining information accuracy and consistency.

[1144] "Business Data" means information entered or uploaded by a User in the course of performing a Business, including text, documents, images, and other file formats.

[1145] A "terminal" is an electronic device operated by a user to input or upload business data, and includes a personal computer, tablet, smartphone, etc.

[1146] A "server" is a central processing unit that analyzes received business data and instructs the AI ​​model to generate materials, and has the ability to communicate with multiple terminals via a network.

[1147] A "generative AI model" is an artificial intelligence model that automatically generates handover documents, FAQs, and training materials based on input data, and analyzes data using natural language processing and machine learning techniques.

[1148] "Handover materials" are documents created by a generative AI model for the purpose of business handover, saved in Markdown format or similar, and containing an overview of the business and detailed procedures.

[1149] An "FAQ" is a collection of frequently asked questions and their answers about specific tasks or products, and is automatically generated by a generative AI model that analyzes past email and chat history.

[1150] "Training materials" are learning materials created by a generative AI model based on work procedures and workflows, and are intended to support the education of new employees and newly appointed personnel.

[1151] "Upload" refers to the act of a user sending data to a server using a terminal, and includes the operation of selecting data through a file selection or input form and pressing the send button.

[1152] "Packetization" refers to the process of dividing the data to be transmitted into a series of data packets and converting them into a format that can be efficiently transmitted over a network.

[1153] "API" is an abbreviation for Application Programming Interface, which enables data communication between servers and terminals, and sends and receives data using standardized communication protocols and data formats.

[1154] "Analysis" refers to the process by which the server or generative AI model analyzes the data it receives and extracts the necessary information, including operations such as formatting, classifying, and summarizing the data.

[1155] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[1156] System configuration

[1157] The system consists of the following major hardware and software components:

[1158] Terminal: A device such as a personal computer, tablet, or smartphone that allows a user to input and upload data.

[1159] Server: A central processing unit that analyzes data and instructs the generative AI model to generate materials.

[1160] Generative AI model: An artificial intelligence model that automatically generates handover materials, FAQs, and training materials based on business data.

[1161] Network: The infrastructure for data communication between devices and servers.

[1162] An embodiment of a handover document creation system

[1163] 1. User enters and uploads business data:

[1164] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user can input keywords such as "marketing strategy" or "advertising cost calculation" and upload related PDF files.

[1165] 2. The device sends the data to the server:

[1166] The terminal converts the data entered or uploaded by the user into the appropriate format and sends it to the server, where it is packetized and transmitted using the HTTPS protocol.

[1167] 3. The server instructs the generative AI model:

[1168] The server analyzes the received business data and instructs the AI ​​model to generate handover documents. The specific prompt is something like "Create handover documents."

[1169] 4. The generative AI model generates handover materials:

[1170] The generative AI model analyzes the provided data and automatically generates handover documents, which are saved in Markdown format.

[1171] 5. The server sends the generated data to the device:

[1172] The server compiles the handover materials received from the generative AI model and sends them to the user's device, where the user can view the generated materials.

[1173] 6. User reviews and edits the document:

[1174] The user can check the generated handover documents on the device and edit them as necessary. The device is provided with a Markdown editor and preview function.

[1175] FAQ creation system embodiment

[1176] 1. User uploads past email and chat history:

[1177] Users can use their devices to upload past email and chat history, for example, uploading customer support email history.

[1178] 2. The device sends the history data to the server:

[1179] The device converts the uploaded data into an appropriate format and sends it to the server.

[1180] 3. The server instructs the generative AI model to generate the FAQ:

[1181] The server analyzes the received data and instructs the generative AI model to generate an FAQ, using prompts such as "Generate an FAQ."

[1182] 4. The generative AI model generates the FAQ:

[1183] The generative AI model analyzes historical data and automatically generates FAQs and their answers, such as questions like "What is the product warranty period?"

[1184] 5. The server sends the generated FAQ to the device:

[1185] The server compiles the generated FAQs and sends them to the user's terminal.

[1186] 6. User checks and edits FAQ:

[1187] The user checks the generated FAQ on the terminal and makes corrections as necessary.

[1188] Embodiment of training support system

[1189] 1. User uploads a procedure or workflow:

[1190] Users use the terminal to input or upload work procedures and workflow diagrams. For example, they can upload procedures such as "procedures for visiting customers" and "how to write sales reports."

[1191] 2. The device sends business data to the server:

[1192] The uploaded data is sent from the terminal to the server.

[1193] 3. The server instructs the generative AI model to generate training materials:

[1194] The server analyzes the received data and instructs the generative AI model to generate training materials.

[1195] 4. The generative AI model generates training materials:

[1196] The generative AI model analyzes procedures and workflows to automatically generate training materials, such as "key points to remember when visiting customers."

[1197] 5. The server sends the generated training materials to the device:

[1198] The server compiles the generated training materials and sends them to the user's terminal.

[1199] 6. User confirms and provides materials:

[1200] The user checks the training materials generated on the terminal, verifies that there are no problems, and then provides them to the new person in charge.

[1201] Example prompt

[1202] 1. When creating handover documents:

[1203] Please compile past business data related to "marketing strategies" and "advertising cost calculations" and prepare handover materials.

[1204] 2. For creating FAQs:

[1205] Generate a list of frequently asked questions and answers based on the past year's worth of customer support emails.

[1206] 3. For training materials:

[1207] Create training materials based on the procedures for "Customer Visit Procedures" and "How to Write a Sales Report."

[1208] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[1209] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1210] Processing steps of the handover document creation system

[1211] Step 1:

[1212] Users use their devices to input or upload business data keywords and related documents. Specifically, they enter keywords such as "marketing strategy" or "advertising cost calculation" into a text box through the device's web interface, and select the related PDF file from the file upload dialog. The input here is the keywords and PDF file, and the output is the data stored on the device.

[1213] Step 2:

[1214] The terminal packets business data entered by the user and sends it to the server. Specifically, a script running on the terminal reads the entered text data or uploaded files and converts it into an appropriate format, such as JSON. The data is then sent to the server via an API using HTTPS. The input here is JSON-formatted data, and the output is that data being sent to the server.

[1215] Step 3:

[1216] The server analyzes the received business data and instructs the generative AI model to create handover documents. Specifically, the server formats and validates the data, checking for inappropriate data. After analyzing the data, it sends a request to the generative AI model along with a prompt such as "Create handover documents." The input is the processed business data and the prompt, and the output is a request sent to the generative AI model.

[1217] Step 4:

[1218] The generative AI model generates handover materials based on the received data. Specifically, the generative AI model analyzes business data, extracts relevant information, and compiles it into a Markdown-formatted document. For example, the "Marketing Strategy" section contains content based on past cases and successful strategies. The input is business data and prompt text, and the output is the generated Markdown-formatted handover materials.

[1219] Step 5:

[1220] The server sends the handover materials received from the generative AI model to the user's device. Specifically, the generated materials are linked to the user ID and session information, stored in a temporary file, and notified to the user's device via a notification API. The notification includes a download link for the materials. The input is the generated materials, and the output is a notification sent to the user's device.

[1221] Step 6:

[1222] The user checks and edits the handover materials generated on the device. Specifically, the user checks the content using the Markdown editor and preview function displayed on the device and makes corrections as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated material, and the output is the corrected final material.

[1223] FAQ creation system processing steps

[1224] Step 1:

[1225] The user uploads past email and chat history using the device. Specifically, the user selects and uploads the past year's worth of email history from the "Customer Support" folder through the device's file upload dialog. The input is an email history file, and the output is the history data stored on the device.

[1226] Step 2:

[1227] The terminal packetizes the uploaded history data and sends it to the server. Specifically, a script on the terminal parses the history data and converts it into an appropriate format such as CSV or JSON. It then sends it to the server via API using the HTTPS protocol. The input is the converted history data, and the output is the data sent to the server.

[1228] Step 3:

[1229] The server analyzes the received history data and instructs the generative AI model to generate FAQs. Specifically, the server organizes the history data by theme and sends a request to the generative AI model along with a prompt such as "Generate an FAQ." The input is the organized history data and the prompt, and the output is a request sent to the generative AI model.

[1230] Step 4:

[1231] The generative AI model generates FAQs based on historical data. Specifically, the generative AI model analyzes historical data and automatically generates frequently asked questions and their answers. For example, a question and answer about the "warranty period of a product" is created. The input is historical data and prompt text, and the output is the generated FAQ.

[1232] Step 5:

[1233] The server sends the generated FAQ to the user's device. The generated FAQ is stored as a temporary file, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated FAQ, and the output is the notification sent to the user's device.

[1234] Step 6:

[1235] The user checks and edits the generated FAQ on the device. Specifically, the user checks the questions and answers in the FAQ editing interface displayed on the device, and makes corrections or additions as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated FAQ, and the output is the final, modified FAQ.

[1236] Training support system processing steps

[1237] Step 1:

[1238] Users use their devices to upload work procedures and workflows. Specifically, they select and upload procedures such as "Procedures for visiting customers" or "How to write sales reports" from the device's file upload dialog. The input is the procedure file, and the output is data stored on the device.

[1239] Step 2:

[1240] The terminal packetizes the uploaded business data and sends it to the server. Specifically, a script on the terminal analyzes the business data, converts it into an appropriate format, and sends it to the server using the HTTPS protocol. The input is the converted business data, and the output is the data sent to the server.

[1241] Step 3:

[1242] The server analyzes the received business data and instructs the generative AI model to generate training materials. Specifically, the server analyzes the procedure manual and business flow and sends a request to the generative AI model along with a prompt. The input is the procedure manual data and the prompt, and the output is a request sent to the generative AI model.

[1243] Step 4:

[1244] The generative AI model generates training materials based on the data it receives. Specifically, the generative AI model analyzes procedures and business flows, and automatically generates educational presentation materials and training manuals. For example, it creates materials such as "Points to remember when visiting customers" and "How to use sales report templates." The input is business data and prompts, and the output is the generated training materials.

[1245] Step 5:

[1246] The server sends the generated training materials to the user's device. The generated materials are stored as temporary files, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated training materials, and the output is a notification of the sending to the user's device.

[1247] Step 6:

[1248] The user checks and provides the generated training materials on the terminal. Specifically, the user checks the training materials on the terminal, confirms that there are no problems, and then prepares to provide them to the new person in charge. The user reviews the materials and checks for errors or omissions. The input is clicking a link in the generated training materials, and the output is the final, confirmed training materials.

[1249] (Application example 1)

[1250] 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."

[1251] In modern manufacturing, rapid training and efficient implementation of new work procedures and workflows are key challenges. In particular, in-factory worker training often relies on manual explanations and training, which requires time and effort. Furthermore, there is a lack of technological means to efficiently manage work-related data and automatically generate handover and training materials based on that data. This creates a need for rapid and accurate training of new work procedures and workflows.

[1252] 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.

[1253] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and executing a generative model for automatically generating handover materials, means for outputting the generated handover materials, means for a factory worker to input or upload work procedures and workflows to a robot, means for the robot to send data to a central server, means for the central server to instruct the generative AI model to generate training materials, means for the generative AI model to generate training materials, means for the central server to send the generated training materials to the robot, and means for the robot to display the materials and for the worker to review and use. This enables factory workers to efficiently receive training on new work procedures and workflows.

[1254] "Business Data" refers to business-related information, documents, procedures, or business flows that are entered or uploaded by a user.

[1255] A "generative model" refers to an algorithm or machine learning model that analyzes data entered by a user and automatically generates materials and answers.

[1256] A "generative AI model" refers to an advanced model that uses artificial intelligence to analyze data entered by users and automatically generate materials.

[1257] "Handover materials" refers to documents that compile the information and procedures necessary to hand over work to another person in charge.

[1258] "Central Server" refers to a central computing device for remote data analysis and management.

[1259] "Training Materials" means educational documents and materials used to learn new business procedures and workflows.

[1260] A "business procedure manual" is a document that contains specific procedures and instructions for carrying out a specific task.

[1261] "Business flow" refers to a flowchart or diagram that shows the series of processes and steps by which a specific business is carried out.

[1262] A "robot" is an automated machine used in factories that has the ability to analyze data entered by workers and display materials.

[1263] A "display screen" refers to a display device installed on a robot or terminal, which visually displays generated materials and information.

[1264] "Factory workers" refer to employees who are engaged in tasks such as machine operation and assembly work within a factory.

[1265] This invention is a system that supports efficient training and job handover for factory workers. Specifically, the system allows users to provide work procedures and workflows, and a central server automatically generates training materials using a generative AI model based on these. The materials are then displayed by a robot.

[1266] Program processing explanation

[1267] Hardware and software used

[1268] Robot terminal: Robots used in factories are equipped with a display screen that provides an interface for workers to input work procedures and workflows.

[1269] Central server: A computing device for data management and processing that processes received business data and instructs the generative AI model on how to analyze it. Specific software includes a RESTful API for transferring data via API.

[1270] Generative AI models: Use advanced artificial intelligence models, such as OpenAI's GPT-4, to analyze the information you provide and automatically generate training and handover materials.

[1271] Specific program behavior

[1272] 1. Data entry: Factory workers use robotic terminals to input or upload new work procedures and workflows, such as "new assembly line procedures" or "machine maintenance workflows."

[1273] 2. Data transmission: The robot terminal transmits the entered or uploaded data to the central server. The data is transmitted using an HTTP POST request.

[1274] 3. Data analysis and material generation: The central server provides the received data to the generative AI model and instructs it to generate training materials. The generative AI model automatically generates training materials based on the data provided by the user.

[1275] 4. Material transmission and display: The generated training materials are sent back to the robot terminal by the central server, and the robot terminal displays the materials on the display screen for factory workers to review and use.

[1276] Adding specific examples

[1277] Prompt Sentence Examples

[1278] Please generate training materials based on the following operating procedures:

[1279] 1. Assembly line preparation

[1280] 2. Safety Check

[1281] 3. Machine start-up procedure

[1282] 4. Production line operation method

[1283] …

[1284] By providing the above prompts to the generative AI model, the model can analyze the user's work procedures and automatically generate relevant training materials, allowing factory workers to quickly master the new work procedures.

[1285] As described above, the present invention is a system that utilizes a generative AI model based on business data provided by the user to automatically generate and efficiently display business handover and training materials, thereby improving business efficiency and enabling rapid adaptation to new business operations.

[1286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1287] Step 1:

[1288] Users use the robot terminal to input or upload work procedures and workflows. The input data includes specific details of the procedures and workflows, such as "new assembly line procedures" or "machine maintenance flow," in text or PDF format.

[1289] Step 2:

[1290] The robot terminal sends the input or uploaded business data to the central server. To do this, the robot terminal uses an HTTP POST request to send the data to the server via an API. The input data is packetized, converted into the appropriate format, and then sent.

[1291] Step 3:

[1292] The server provides the received data to the generative AI model and instructs it to generate training materials. The server sends the data to the generative AI model using an HTTP request, including a prompt such as "Please generate training materials based on the following business procedure manual."

[1293] Step 4:

[1294] The generative AI model analyzes the provided data and automatically generates training materials. The generative AI model creates specific training materials and manuals based on the work procedures entered by the user. For example, it generates materials including content such as "assembly line preparation," "safety checks," and "machine startup procedures."

[1295] Step 5:

[1296] The server sends the generated training materials to the robot terminal, converts the materials received from the generative AI model into an appropriate format, and sends them back to the robot terminal using an HTTP POST request.

[1297] Step 6:

[1298] The robot terminal displays the generated training materials on the display screen for the user to review and use. The robot terminal then displays the received materials on the display screen, allowing factory workers to review work procedures based on the materials and apply them to their actual work.

[1299] 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.

[1300] This invention analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine.By using the emotion engine, it is possible to analyze user emotions and reflect them in the generated materials.

[1301] An embodiment of a handover document creation system

[1302] Program processing explanation

[1303] 1. The user enters business data

[1304] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[1305] 2. Emotion engine recognizes emotions

[1306] The emotion engine recognizes the emotion of the user as they type. For example, the emotion engine analyzes the emotions (joy, anger, sadness, etc.) that the user is expressing while typing text.

[1307] 3. The device sends the data to the server

[1308] The device sends the input business data and the recognized emotion data to the server, packetizes the data, and sends a request to the server via API.

[1309] 4. The server instructs the generative AI model

[1310] The server analyzes the received business data and emotional data and issues instructions to the AI ​​model to generate documents. Specifically, it instructs the AI ​​model to "create handover documents," and also provides the emotional data to the AI ​​model.

[1311] 5. Generative AI model generates handover documents

[1312] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotional data. The AI ​​model adjusts the conversation scenario to reflect the emotional data, and converts it into a Markdown-formatted document to create the materials.

[1313] 6. The server sends the generated data to the terminal.

[1314] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[1315] 7. The user checks and edits the document

[1316] The user checks the handover materials generated on the terminal and edits and uses them as necessary, for example, by adding additional information to the materials or adjusting the layout.

[1317] FAQ creation system embodiment

[1318] 1. Users upload their past email and chat history

[1319] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[1320] 2. Emotion engine recognizes emotions

[1321] The emotion engine recognizes user emotions from email and chat history, for example, extracting emotions (e.g., customer dissatisfaction or satisfaction) within each email or chat.

[1322] 3. The device sends the history data to the server

[1323] Uploaded email and chat history data and recognized emotion data are sent from the device to the server, where the data is analyzed and converted into a data format suitable for sending to the server.

[1324] 4. The server instructs the generative AI model to generate FAQs

[1325] The server provides the received history data and emotion data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the emotion data to the model.

[1326] 5. Generative AI model generates FAQs

[1327] The generative AI model analyzes the provided historical and sentiment data to automatically generate frequently asked questions and their answers. For example, based on customer sentiment analysis, it generates FAQs such as "About the product warranty period" and "How to process a refund."

[1328] 6. The server sends the generated FAQ to the device.

[1329] The server sends the generated FAQ and its answers to the user's terminal, where the data is repacketized and sent to the user's terminal.

[1330] 7. Users check and edit the FAQ

[1331] The user checks the FAQ generated on the terminal, makes corrections or additions as necessary, and provides it to the new support staff.

[1332] Embodiment of training support system

[1333] 1. The user inputs or uploads a business procedure manual or workflow.

[1334] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1335] 2. Emotion engine recognizes emotions

[1336] The emotion engine recognizes the user's emotions in the procedure manual or workflow. For example, it analyzes the emotions (satisfaction, anxiety, etc.) when creating the procedure manual.

[1337] 3. The device sends business data to the server

[1338] The uploaded business data and recognized emotion data are sent from the device to the server, which then packets the data and sends it to the server.

[1339] 4. The server instructs the AI ​​model to generate training materials.

[1340] The server provides the received business data and emotional data to the generative AI model and instructs it to generate training materials. Specifically, it instructs the AI ​​model to "create training materials" and provides the emotional data to the model.

[1341] 5. Generative AI models generate training materials

[1342] The generative AI model analyzes business data and emotional data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[1343] 6. The server sends the generated training materials to the device.

[1344] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[1345] 7. User confirms and provides materials

[1346] The user checks the training materials generated on the terminal, checks the contents as necessary, and then provides the materials to the new person in charge.

[1347] As described above, this invention utilizes a generative AI model and an emotion engine to automatically generate materials based on data and emotion data entered by the user, and provides the results to the user, thereby streamlining business handover and enabling rapid adaptation to new business operations.

[1348] The processing flow will be explained below.

[1349] Processing steps of the handover document creation system

[1350] Creating handover documents

[1351] Step 1:

[1352] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[1353] Step 2:

[1354] The emotion engine recognizes the user's emotions. The emotion engine analyzes emotions such as "stress" and "expectation" from the text entered by the user and the contents of related documents.

[1355] Step 3:

[1356] The device sends the input business data and emotion data to the server. Specifically, the encoded data is packetized and sent to the server via an API.

[1357] Step 4:

[1358] The server provides the received business data and emotion data to the generative AI model and instructs it to generate documents. The server then sends the data to the AI ​​model along with the instruction to "create handover documents."

[1359] Step 5:

[1360] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotion data. The materials are created in Markdown format and include explanations and points of caution that reflect the emotion data.

[1361] Step 6:

[1362] The server sends the generated handover materials to the terminal. The server compiles the materials, packets them into a format that is easy for the user to understand, and sends them to the terminal.

[1363] Step 7:

[1364] The user checks the generated handover documents on the terminal and edits them as necessary, for example, by correcting the text of the documents or adding additional information.

[1365] FAQ creation

[1366] Step 1:

[1367] The user uses a device to upload past email data and chat history related to the work being handed over. For example, upload the past year's worth of customer support email history.

[1368] Step 2:

[1369] The emotion engine recognizes user emotions from email and chat history, extracting emotions such as "satisfied" or "dissatisfied" from customer interactions.

[1370] Step 3:

[1371] The device sends the uploaded history data and emotion data to the server, which analyzes the encoded data, converts it into a format suitable for transmission, and sends it to the server.

[1372] Step 4:

[1373] The server instructs the generation AI model to generate an FAQ. The server instructs the AI ​​model to "create an FAQ" and provides historical data and emotional data to the AI ​​model.

[1374] Step 5:

[1375] Generative AI models analyze historical and sentiment data to automatically generate frequently asked questions (FAQs) and their answers, for example, by taking customer sentiment into account.

[1376] Step 6:

[1377] The server sends the generated FAQ to the terminal. The server compiles the information, packets it into an appropriate format, and sends it to the user's terminal.

[1378] Step 7:

[1379] The user checks the generated FAQ on the device and makes corrections or additions as necessary, for example, adding new questions or making changes to clarify the answers.

[1380] Training Support

[1381] Step 1:

[1382] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, they can upload procedure manuals such as "procedures for visiting customers" or "how to write sales reports."

[1383] Step 2:

[1384] The emotion engine recognizes the user's emotions from the procedure manual and business flow, analyzing emotions such as "sense of relief" and "confusion" from the contents and explanations of the procedure manual.

[1385] Step 3:

[1386] The device sends business data and emotion data to the server, which encodes the data into packets and sends them to the server via API.

[1387] Step 4:

[1388] The server instructs the AI ​​model to generate training materials. The server instructs the AI ​​model to "create training materials" and provides data and emotional information to the AI ​​model.

[1389] Step 5:

[1390] The generative AI model analyzes business data and emotion data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[1391] Step 6:

[1392] The server sends the generated training materials to the terminal, where it compiles the materials, packets them into an appropriate format for delivery to the user, and transmits them.

[1393] Step 7:

[1394] The user checks the training materials generated on the terminal, and after reviewing the content as necessary, provides the materials to the new personnel, for example by editing the materials to add additional explanations.

[1395] Through this process, handover materials, FAQs, and training materials that reflect the user's feelings are automatically generated. This system makes the handover process more efficient and enables rapid business adaptation.

[1396] Example 2

[1397] 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."

[1398] In today's business environment, the creation of efficient handover documents, frequently asked questions (FAQs), and training materials is a key challenge. However, creating these documents manually requires a significant amount of time and effort, and often loses emotional nuance and context. It is also difficult to effectively utilize past communications and operating procedures. Therefore, there is a need for automated, efficient, and emotionally sensitive document generation.

[1399] 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.

[1400] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and recognizing emotions, means for transmitting the input data and emotional data from the user terminal to the server, means for automatically generating handover materials, FAQs, and training materials using a generative AI model based on the received data and emotional data, and means for transmitting and outputting the generated materials to the user terminal. This improves the efficiency of automatic generation of work-related materials and enables the creation of more appropriate materials that take user emotions into consideration.

[1401] "Data related to business" refers to information related to business input by a user, and includes all data necessary for business execution, including text, documents, keywords, and the like.

[1402] An "emotion engine" is an algorithm that analyzes text and data entered by users and recognizes and classifies emotions.

[1403] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning techniques to automatically generate specific materials from input data.

[1404] "Handover materials" are documents and guides created to efficiently and accurately hand over work to another person.

[1405] "Frequently Asked Questions (FAQ)" refers to a collection of frequently asked questions and answers from users regarding a particular business or service.

[1406] "Training materials" are educational materials used to enable new personnel and staff to carry out their duties smoothly, and include procedures and workflow diagrams.

[1407] A "server" is a device that receives, analyzes, stores, and transmits data over a network, and in this invention also executes the generative AI model.

[1408] "Terminal" refers to a device that allows a user to input, send, and receive business data and history data, and display the generated materials.

[1409] "Receiving means" refers to a function or process for receiving data entered by a user.

[1410] "Transmission means" refers to a function or process for transmitting received data or generated materials to a server or user terminal.

[1411] "Output means" refers to the functions and processes for providing the generated materials in a format that can be displayed and edited on the user's terminal.

[1412] "Interface" refers to the operation screen or input means that allows users to input business data and check and edit generated documents.

[1413] This invention is a system that uses business data and past communication history entered by users to automatically generate handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine. The implementation method is described in detail below.

[1414] System Configuration

[1415] This system consists of a terminal operated by the user, a server that analyzes and processes data, a generative AI model, and an emotion engine. The terminal is a device into which the user inputs business data, past emails, chat history, etc., and includes PCs and tablets. The server is a central processing unit that analyzes the data and runs the generative AI model. The emotion engine is software that analyzes emotions from user input data and uses NLP (Natural Language Processing) algorithms.

[1416] Generate handover documents

[1417] 1. Input: The user uses the terminal to input business data, either manually using text fields or by uploading relevant documents using drag-and-drop. For example, they can upload keywords such as "marketing strategy" and "advertising cost calculation" and relevant documents.

[1418] 2. Sentiment analysis: The emotion engine analyzes the user's emotions (joy, anger, sadness, etc.) from the input text in real time. This allows not only the text itself but also the user's emotions to be obtained as data.

[1419] 3. Data transmission: The device packets the input business data and recognized emotion data and sends them to the server via API. For example, the data is sent in JSON format using an HTTP request.

[1420] 4. Document generation: The server issues instructions to the AI ​​model to generate documents based on the received data. The prompt text includes specific instructions and emotional data. For example, it might say, "Create handover documents, taking into account the emotional data."

[1421] 5. Output: The generative AI model generates handover materials, converts them into Markdown documents, and sends them back to the server. The server then compiles them and sends them back to the device. The user can review these documents and edit them as needed.

[1422] FAQ generation

[1423] 1. Input: The user uploads past email and chat history from their device. For example, they upload customer support email history from the past year.

[1424] 2. Sentiment Analysis: The sentiment engine analyzes the content of emails and chats to identify the sentiment within each message.

[1425] 3. Data transmission: The device transmits history data and emotion data to the server.

[1426] 4. FAQ generation: The server instructs the AI ​​model to generate FAQs. For example, it uses a prompt such as "Generate frequently asked questions and their answers from past emails."

[1427] 5. Output: The generative AI model creates FAQs, which are then compiled by the server and sent to the user's device. The user can then review the generated FAQs and edit them as necessary.

[1428] Generate training materials

[1429] 1. Input: The user inputs the work procedure manual or workflow diagram from the terminal or uploads it in PDF format. For example, they can upload "procedures for visiting customers" or "how to write sales reports."

[1430] 2. Sentiment Analysis: The emotion engine analyzes emotions in procedures and flow diagrams. For example, it analyzes "anxiety" from the text in a procedure manual.

[1431] 3. Data transmission: The device transmits the business data and the recognized emotion data to the server.

[1432] 4. Material generation: The server instructs the AI ​​model to generate training materials, for example, by using a prompt such as "Create training materials."

[1433] 5. Output: The generative AI model generates training materials, which are then compiled by the server and sent to the user's device. The user can review the materials and edit them as necessary before providing them to the new person in charge.

[1434] Examples of specific examples and prompts

[1435] As a concrete example, consider the case where a user is creating handover documents for a "marketing strategy." When the user uploads the relevant documents, the emotion engine recognizes "joy," and the server sends a prompt to the generative AI model instructing it to "create handover documents, taking into account the emotion data." The generated documents are returned to the terminal in Markdown format, where the user can review and edit them.

[1436] Example prompt sentence:

[1437] "Create a handover document for your marketing strategy. Consider sentiment data."

[1438] In this way, the present invention is a system that enables efficient creation of materials that take emotions into consideration based on business data and emotion data.

[1439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1440] Step 1:

[1441] The user enters business data

[1442] The user uses the device to input keywords and related documents related to the business to be handed over or upload them by drag and drop. Specifically, the user may input keywords such as "marketing strategy" or "calculating advertising costs" into the text field on the device, or upload a PDF file of a marketing report. The input of this action is business data, and the output is business data saved on the device.

[1443] Step 2:

[1444] Emotion engine recognizes emotions

[1445] The device provides input business data to the emotion engine, which analyzes the emotions in the text. The emotion engine uses NLP algorithms to extract emotions such as "joy," "anger," and "sadness" from the input text. For example, if a user types "This project is great," the emotion engine recognizes "joy." The input for this operation is business data, and the output is the recognized emotion data.

[1446] Step 3:

[1447] The device sends the data to the server

[1448] The device packets the business data and the recognized emotion data and sends them to the server using an HTTP request. The data is packetized in JSON format and sent to the server's API endpoint. For example, the following JSON format data is sent:

[1449] json

[1450] {

[1451] "Business Data": "Marketing Strategy",

[1452] "Emotion Data": "Joy"

[1453] }

[1454] The inputs for this operation are task data and emotion data, and the output is the data sent to the server.

[1455] Step 4:

[1456] The server instructs the generative AI model

[1457] The server analyzes the received business data and emotion data and issues instructions to the generative AI model to generate materials. Specifically, it generates a prompt sentence and instructs the generative AI model to "create handover materials, taking into account the emotion data." For example, the generated prompt sentence is as follows:

[1458] "Create a handover document for your marketing strategy. Consider sentiment data."

[1459] The inputs for this behavior are incoming data and emotion data, and the output is instructions to the generative AI model.

[1460] Step 5:

[1461] Generative AI model generates handover documents

[1462] The generative AI model automatically generates handover materials based on the provided business data and emotion data. The generative AI model uses deep learning to analyze the input data and generate handover materials in Markdown format. For example, a document containing detailed explanations and procedures for a "marketing strategy" is generated. The input for this operation is a prompt and data, and the output is the generated handover materials.

[1463] Step 6:

[1464] The server sends the generated data to the terminal.

[1465] The server collects the handover data received from the generative AI model, repackets it, and sends it to the user's device. The data is then repacketized in an HTTP request and sent to the user's device. For example, the following Markdown-formatted data is sent:

[1466] markdown

[1467] Marketing Strategy

[1468] This article will introduce you to the key points of your marketing strategy...

[1469] The input of this operation is the generated handover material, and the output is the material sent to the user terminal.

[1470] Step 7:

[1471] Users can view and edit documents

[1472] The user reviews the handover materials generated on their device and makes edits as needed. They use document editing software or a web application to add content to the materials or adjust their layout. For example, they may add specific figures or graphs related to a marketing strategy. The input for this operation is the received handover materials, and the output is the edited final materials.

[1473] (Application example 2)

[1474] 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."

[1475] In today's business environment, efficient work handovers and improved customer support quality are required. However, traditional manual handover materials and FAQ generation require a significant amount of time and effort, and are difficult to consider individual emotions. Furthermore, preparing training materials is also a significant burden, making it difficult for new employees to quickly adapt to their work. To solve these problems, a system is needed that automatically analyzes information such as business data and customer interaction history, and generates optimal materials based on emotional data.

[1476] 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.

[1477] In this invention, the server includes a means for receiving data related to work input by a user, a means for analyzing emotions based on the received data, a means for analyzing the data and emotions and executing a generative model for automatically generating handover materials, and a means for outputting the generated handover materials. This makes it possible to create handover materials efficiently and with consideration for emotions based on the work data and emotion data.

[1478] The server also includes a means for receiving past communication history input by the user, a means for analyzing emotions from the received history data, a means for executing a generative model for automatically generating frequently asked questions and their answers based on the analyzed emotions, and a means for outputting the generated frequently asked questions and their answers. This makes it possible to generate optimal FAQs based on past customer interaction history and analyzed emotion data.

[1479] The server further includes a means for receiving a work procedure manual or work flow input by a user, a means for analyzing emotions from the received data, a means for executing a generative model for automatically generating training materials based on the analyzed data and emotions, and a means for outputting the generated training materials, thereby enabling efficient creation of training materials that enable new employees to quickly adapt to their work.

[1480] "Business data" is a general term for information, documents, instructions, procedures, etc. used by users when performing their business.

[1481] "Means for analyzing emotions" refers to a system that extracts emotional elements from user input data and history data, and classifies, evaluates, and analyzes the type and intensity of emotions.

[1482] A "generative model for generating handover materials" is a model used to automatically generate the materials necessary for the next worker to take over the work, based on the received work data and emotion data.

[1483] "Means for outputting generated handover materials" refers to means for providing the handover materials created by the generative model to the user, and refers to outputting them as electronic data or printed materials.

[1484] "Communication history" is a general term for records of past communications such as emails and chats, and includes messages and related information exchanged between a user and other people.

[1485] A "generative model for generating frequently asked questions and their answers" is a model used to automatically generate FAQs (frequently asked questions) and their answers based on communication history and emotional data.

[1486] "Means for outputting the generated frequently asked questions and their answers" refers to the means for providing the FAQs created by the generative model and their answers to users, and refers to outputting them as electronic data or printed material.

[1487] A "work procedure manual" is a document that describes the specific procedures for carrying out a specific task.

[1488] A "business flow" is a diagram or chart that visually represents the flow or process of a business.

[1489] A "generative model for generating training materials" is a model used to automatically generate materials necessary for training new employees, etc., based on business data and emotion data.

[1490] "Means for outputting generated training materials" refers to means for providing training materials created by a generative model to users, and refers to outputting them as electronic data or printed materials.

[1491] MODE FOR CARRYING OUT THE INVENTION

[1492] This invention relates to a business support system that handles business data, past communication history, business procedure manuals, and business flows. Specifically, it is a system that analyzes data entered by users and automatically generates handover documents, FAQs, and training materials using a generative AI model and an emotion engine.

[1493] System Configuration

[1494] The system includes the following main components:

[1495] 1. User terminal: Provides an interface for inputting or uploading business data, past communication history, business procedures, and business flows. For example, devices such as PCs, smartphones, and tablets are used.

[1496] 2. Server: This is the central system that receives data, parses it, analyzes sentiment, runs generative AI models, and distributes the generated materials.

[1497] 3. Emotion engine: Software for analyzing emotions from incoming and historical data. For example, the transformers library and its emotion analysis module are used.

[1498] 4. Generative AI model: An AI model for generating handover materials, FAQs, and training materials based on the received data and sentiment data. For example, OpenAI's text-davinci-003 model is used.

[1499] Program processing description

[1500] The server performs the following process:

[1501] 1. Data reception and preprocessing: The system receives business data, communication history, business procedures, and business flows entered by the user from their terminal. The user terminal converts these into a data format and sends them to the server.

[1502] 2. Sentiment Analysis: The received data is analyzed by the emotion engine to extract the emotional components in the data. For example, the user's input is classified as "positive," "negative," or "neutral."

[1503] 3. Execution of the generative AI model: Based on the results of the sentiment analysis and business data, the generative AI model generates handover materials, FAQs, and training materials. For example, it issues instructions using prompt sentences like the following:

[1504] text

[1505] Context: {Past data and historical information}

[1506] User Input: {User's specific question or request}

[1507] Emotion: {Parsed emotion label}

[1508] Generate a helpful response with appropriate sentiment.

[1509] 4. Creation and distribution of materials: The created materials are sent to the user's terminal via the server again, allowing the user to view, edit, and use them.

[1510] Specific examples

[1511] For example, if a user enters a comment such as "I can't set up the product properly, it's stressful," the system will operate as follows:

[1512] 1. Receive the data entered by the user and assign the emotion label "negative."

[1513] 2. Incorporate emotion labels and input data into prompt sentences.

[1514] text

[1515] Context: Past customer support history

[1516] User Input: I'm having trouble setting up my product. What should I do?

[1517] Emotion: Negative

[1518] Generate a helpful response with appropriate sentiment.

[1519] 3. The generative AI model generates an appropriate response and provides it to the user device via the server.

[1520] In this way, the system can analyze emotions and automatically generate optimal materials based on the user's work data, communication history, work procedures, and workflow. This not only dramatically improves the efficiency of work handovers and customer support, but also automates the creation of training materials to help new employees quickly adapt to their work.

[1521] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1522] Step 1:

[1523] Inputting and receiving data via a user terminal

[1524] The user uses the device to input or upload business data, past communication history, work procedures, or work flows. For example, they can upload data about "product configuration problems," past email and chat history, and work procedures. The device receives this data and converts it into the appropriate data format.

[1525] Step 2:

[1526] Data transmission to server and pre-processing

[1527] The device converts data entered or uploaded by the user into packets and sends them to the server via API. Specifically, it sends converted data including business data, communication history, procedure manuals, etc. The server receives this data and stores it in the appropriate storage.

[1528] Step 3:

[1529] Performing sentiment analysis

[1530] The server passes the received data to the emotion engine for emotion analysis. For example, it uses the transformers library to extract and classify emotion elements (e.g., positive, negative, neutral) from the data. The analysis results in emotion labels and their intensities.

[1531] Step 4:

[1532] Prompt generation for generative AI models

[1533] The server generates a prompt sentence to be passed to the generative AI model based on the emotion analysis results and the received data. As a specific example, the following prompt sentence is created:

[1534] text

[1535] Context: Past customer support history

[1536] User Input: I'm having trouble setting up my product. What should I do?

[1537] Emotion: Negative

[1538] Generate a helpful response with appropriate sentiment.

[1539] Step 5:

[1540] Execution of generative AI models and data generation

[1541] The server uses the generated prompt to run a generative AI model (e.g., OpenAI's text-davinci-003), which generates appropriate handover materials, FAQs, or training materials based on the prompt. The generated results are output in text format.

[1542] Step 6:

[1543] Distribution of generated materials

[1544] The server repackets the output from the generative AI model and sends it to the user's device, where the delivered materials can be viewed, edited, and output.

[1545] Step 7:

[1546] User confirmation and use of materials

[1547] The user reviews the received documents on their device, edits the content or adds additional information as needed, and then prints or digitally provides the documents to other personnel. For example, handover documents are likely to include "Troubleshooting Procedures for Configuration Issues" and answers to frequently asked questions.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] [Fourth embodiment]

[1552] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1553] 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.

[1554] 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).

[1555] 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.

[1556] 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.

[1557] 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).

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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."

[1565] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[1566] An embodiment of a handover document creation system

[1567] Program processing explanation

[1568] 1. The user enters business data

[1569] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[1570] 2. The device sends the data to the server

[1571] The terminal sends business data entered or uploaded by the user to the server, including the process of packetizing the input data and sending it to the server via API.

[1572] 3. The server instructs the generative AI model

[1573] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, it passes instructions such as "Create handover documents" to the AI ​​model along with the data.

[1574] 4. Generative AI model generates handover documents

[1575] The generative AI model automatically generates conversation content and materials for handovers based on the provided business data. The generated materials are saved in Markdown format.

[1576] 5. The server sends the generated data to the device.

[1577] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[1578] 6. The user checks and edits the document

[1579] The user checks the handover materials generated on the terminal, and edits and uses them as necessary.

[1580] FAQ creation system embodiment

[1581] 1. Users upload their past email and chat history

[1582] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[1583] 2. The device sends the history data to the server

[1584] Uploaded emails and chat histories are sent from the device to the server, where the device analyzes the data and converts it into an appropriate format for transmission to the server.

[1585] 3. The server instructs the AI ​​model to generate FAQs

[1586] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers.

[1587] 4. Generative AI model generates FAQs

[1588] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers, such as "About the product warranty period" and "How to process a refund."

[1589] 5. The server sends the generated FAQ to the device.

[1590] The server sends the generated FAQ to the user's terminal, allowing the user to view the FAQ.

[1591] 6. Users check and edit the FAQ

[1592] The user can check the FAQ generated on the device and make corrections or additions as necessary.

[1593] Embodiment of training support system

[1594] 1. The user inputs or uploads a business procedure manual or workflow.

[1595] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1596] 2. The device sends business data to the server

[1597] The uploaded business data is sent from the terminal to the server, where it is packetized and sent to the server.

[1598] 3. The server instructs the AI ​​model to generate training materials.

[1599] The server provides the received business procedures and workflow to the generative AI model and instructs it to generate training materials.

[1600] 4. Generative AI models generate training materials

[1601] The generative AI model analyzes provided procedures and workflows to generate educational presentation materials and training manuals, such as "Points to remember when visiting customers" and "How to use sales report templates."

[1602] 5. The server sends the generated training materials to the device.

[1603] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[1604] 6. User confirms and provides materials

[1605] The user checks the generated training materials on the terminal, and after confirming that there are no problems, provides the materials to the new person in charge.

[1606] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[1607] The processing flow will be explained below.

[1608] Processing steps of the handover document creation system

[1609] Creating handover documents

[1610] Step 1:

[1611] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[1612] Step 2:

[1613] The device sends the entered or uploaded business data to the server, which then packets keywords and documents and sends a request to the server via API.

[1614] Step 3:

[1615] The server analyzes the received business data and instructs the AI ​​model to generate documents. Specifically, the server instructs the AI ​​model to "create handover documents" and provides the data to the AI ​​model.

[1616] Step 4:

[1617] The generative AI model automatically generates conversation content and materials for handovers based on business data. The AI ​​model generates a conversation scenario based on the business content and converts it into a Markdown document to create the materials.

[1618] Step 5:

[1619] The server receives the generated handover material and transmits it to the user's terminal. The server repackets the material and transmits it to the user's terminal.

[1620] Step 6:

[1621] The user checks the handover documents generated on the device, edits them as necessary, and shares them with subordinates or successors.

[1622] FAQ creation

[1623] Step 1:

[1624] The user uses a device to upload past email data and chat history related to the work to be handed over. For example, the user uploads customer support email history from the past year.

[1625] Step 2:

[1626] The device sends the uploaded email and chat history data to the server, where it analyzes the data and converts it into a data format suitable for sending to the server.

[1627] Step 3:

[1628] The server provides the received historical data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the data to the AI ​​model.

[1629] Step 4:

[1630] The generative AI model analyzes the provided historical data and automatically generates frequently asked questions and their answers. The AI ​​analyzes data patterns and generates FAQs such as "About the product warranty period" and "How to process a refund."

[1631] Step 5:

[1632] The server sends the generated FAQ to the user's terminal. The server repackets the FAQ and sends it to the user's terminal.

[1633] Step 6:

[1634] The user reviews the generated FAQ on the device, corrects or adds answers as needed, and provides it to the new support representative.

[1635] Training Support

[1636] Step 1:

[1637] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1638] Step 2:

[1639] The terminal sends the entered or uploaded procedure manual and business flow data to the server, which then packets the data and sends it to the server.

[1640] Step 3:

[1641] The server provides the received business data to the generative AI model and instructs it to generate training materials. Specifically, the server instructs the AI ​​model to "create training materials" and provides the data to the AI ​​model.

[1642] Step 4:

[1643] The generative AI model analyzes the provided procedure manuals and business flows to generate educational presentation materials and training manuals. The AI ​​analyzes the procedure manuals and business flows to generate materials such as "key points to remember when visiting customers" and "how to use sales report templates."

[1644] Step 5:

[1645] The server receives the generated training materials and sends them to the user's terminal. The server repackets the materials and sends them to the user's terminal.

[1646] Step 6:

[1647] The user checks the training materials generated on the terminal, and after checking the contents as necessary, provides the materials to the new personnel.

[1648] Through the above process, the efficiency of business handover and rapid business adaptation can be achieved.

[1649] Example 1

[1650] 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."

[1651] Currently, many companies face inefficient manual processes for handing over tasks, creating FAQs, and preparing training materials. It's also difficult to maintain the accuracy and consistency of information, making it difficult to quickly hand over tasks to new staff. This leads to reduced operational efficiency and the risk of information leaks.

[1652] 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.

[1653] In this invention, the server includes means for receiving business data entered by a user, means for transmitting the received business data to the server, means for the server to instruct the generation AI model to analyze and generate materials, means for the generation AI model to generate handover materials based on the analysis results, means for transmitting the generated handover materials from the server to the user's terminal, and means for the user to check and edit the generated materials on the terminal. This enables efficient business handover, rapid creation of FAQs, and automatic generation of training materials, enabling rapid handover to new personnel while maintaining information accuracy and consistency.

[1654] "Business Data" means information entered or uploaded by a User in the course of performing a Business, including text, documents, images, and other file formats.

[1655] A "terminal" is an electronic device operated by a user to input or upload business data, and includes a personal computer, tablet, smartphone, etc.

[1656] A "server" is a central processing unit that analyzes received business data and instructs the AI ​​model to generate materials, and has the ability to communicate with multiple terminals via a network.

[1657] A "generative AI model" is an artificial intelligence model that automatically generates handover documents, FAQs, and training materials based on input data, and analyzes data using natural language processing and machine learning techniques.

[1658] "Handover materials" are documents created by a generative AI model for the purpose of business handover, saved in Markdown format or similar, and containing an overview of the business and detailed procedures.

[1659] An "FAQ" is a collection of frequently asked questions and their answers about specific tasks or products, and is automatically generated by a generative AI model that analyzes past email and chat history.

[1660] "Training materials" are learning materials created by a generative AI model based on work procedures and workflows, and are intended to support the education of new employees and newly appointed personnel.

[1661] "Upload" refers to the act of a user sending data to a server using a terminal, and includes the operation of selecting data through a file selection or input form and pressing the send button.

[1662] "Packetization" refers to the process of dividing the data to be transmitted into a series of data packets and converting them into a format that can be efficiently transmitted over a network.

[1663] "API" is an abbreviation for Application Programming Interface, which enables data communication between servers and terminals, and sends and receives data using standardized communication protocols and data formats.

[1664] "Analysis" refers to the process by which the server or generative AI model analyzes the data it receives and extracts the necessary information, including operations such as formatting, classifying, and summarizing the data.

[1665] This invention provides a system that analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model.

[1666] System configuration

[1667] The system consists of the following major hardware and software components:

[1668] Terminal: A device such as a personal computer, tablet, or smartphone that allows a user to input and upload data.

[1669] Server: A central processing unit that analyzes data and instructs the generative AI model to generate materials.

[1670] Generative AI model: An artificial intelligence model that automatically generates handover materials, FAQs, and training materials based on business data.

[1671] Network: The infrastructure for data communication between devices and servers.

[1672] An embodiment of a handover document creation system

[1673] 1. User enters and uploads business data:

[1674] The user uses the terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user can input keywords such as "marketing strategy" or "advertising cost calculation" and upload related PDF files.

[1675] 2. The device sends the data to the server:

[1676] The terminal converts the data entered or uploaded by the user into the appropriate format and sends it to the server, where it is packetized and transmitted using the HTTPS protocol.

[1677] 3. The server instructs the generative AI model:

[1678] The server analyzes the received business data and instructs the AI ​​model to generate handover documents. The specific prompt is something like "Create handover documents."

[1679] 4. The generative AI model generates handover materials:

[1680] The generative AI model analyzes the provided data and automatically generates handover documents, which are saved in Markdown format.

[1681] 5. The server sends the generated data to the device:

[1682] The server compiles the handover materials received from the generative AI model and sends them to the user's device, where the user can view the generated materials.

[1683] 6. User reviews and edits the document:

[1684] The user can check the generated handover documents on the device and edit them as necessary. The device is provided with a Markdown editor and preview function.

[1685] FAQ creation system embodiment

[1686] 1. User uploads past email and chat history:

[1687] Users can use their devices to upload past email and chat history, for example, uploading customer support email history.

[1688] 2. The device sends the history data to the server:

[1689] The device converts the uploaded data into an appropriate format and sends it to the server.

[1690] 3. The server instructs the generative AI model to generate the FAQ:

[1691] The server analyzes the received data and instructs the generative AI model to generate an FAQ, using prompts such as "Generate an FAQ."

[1692] 4. The generative AI model generates the FAQ:

[1693] The generative AI model analyzes historical data and automatically generates FAQs and their answers, such as questions like "What is the product warranty period?"

[1694] 5. The server sends the generated FAQ to the device:

[1695] The server compiles the generated FAQs and sends them to the user's terminal.

[1696] 6. User checks and edits FAQ:

[1697] The user checks the generated FAQ on the terminal and makes corrections as necessary.

[1698] Embodiment of training support system

[1699] 1. User uploads a procedure or workflow:

[1700] Users use the terminal to input or upload work procedures and workflow diagrams. For example, they can upload procedures such as "procedures for visiting customers" and "how to write sales reports."

[1701] 2. The device sends business data to the server:

[1702] The uploaded data is sent from the terminal to the server.

[1703] 3. The server instructs the generative AI model to generate training materials:

[1704] The server analyzes the received data and instructs the generative AI model to generate training materials.

[1705] 4. The generative AI model generates training materials:

[1706] The generative AI model analyzes procedures and workflows to automatically generate training materials, such as "key points to remember when visiting customers."

[1707] 5. The server sends the generated training materials to the device:

[1708] The server compiles the generated training materials and sends them to the user's terminal.

[1709] 6. User confirms and provides materials:

[1710] The user checks the training materials generated on the terminal, verifies that there are no problems, and then provides them to the new person in charge.

[1711] Example prompt

[1712] 1. When creating handover documents:

[1713] Please compile past business data related to "marketing strategies" and "advertising cost calculations" and prepare handover materials.

[1714] 2. For creating FAQs:

[1715] Generate a list of frequently asked questions and answers based on the past year's worth of customer support emails.

[1716] 3. For training materials:

[1717] Create training materials based on the procedures for "Customer Visit Procedures" and "How to Write a Sales Report."

[1718] As described above, this invention utilizes a generative AI model to automatically generate materials based on data entered by the user, and provides the results to the user, thereby improving the efficiency of business handover and enabling rapid adaptation to new business.

[1719] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1720] Processing steps of the handover document creation system

[1721] Step 1:

[1722] Users use their devices to input or upload business data keywords and related documents. Specifically, they enter keywords such as "marketing strategy" or "advertising cost calculation" into a text box through the device's web interface, and select the related PDF file from the file upload dialog. The input here is the keywords and PDF file, and the output is the data stored on the device.

[1723] Step 2:

[1724] The terminal packets business data entered by the user and sends it to the server. Specifically, a script running on the terminal reads the entered text data or uploaded files and converts it into an appropriate format, such as JSON. The data is then sent to the server via an API using HTTPS. The input here is JSON-formatted data, and the output is that data being sent to the server.

[1725] Step 3:

[1726] The server analyzes the received business data and instructs the generative AI model to create handover documents. Specifically, the server formats and validates the data, checking for inappropriate data. After analyzing the data, it sends a request to the generative AI model along with a prompt such as "Create handover documents." The input is the processed business data and the prompt, and the output is a request sent to the generative AI model.

[1727] Step 4:

[1728] The generative AI model generates handover materials based on the received data. Specifically, the generative AI model analyzes business data, extracts relevant information, and compiles it into a Markdown-formatted document. For example, the "Marketing Strategy" section contains content based on past cases and successful strategies. The input is business data and prompt text, and the output is the generated Markdown-formatted handover materials.

[1729] Step 5:

[1730] The server sends the handover materials received from the generative AI model to the user's device. Specifically, the generated materials are linked to the user ID and session information, stored in a temporary file, and notified to the user's device via a notification API. The notification includes a download link for the materials. The input is the generated materials, and the output is a notification sent to the user's device.

[1731] Step 6:

[1732] The user checks and edits the handover materials generated on the device. Specifically, the user checks the content using the Markdown editor and preview function displayed on the device and makes corrections as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated material, and the output is the corrected final material.

[1733] FAQ creation system processing steps

[1734] Step 1:

[1735] The user uploads past email and chat history using the device. Specifically, the user selects and uploads the past year's worth of email history from the "Customer Support" folder through the device's file upload dialog. The input is an email history file, and the output is the history data stored on the device.

[1736] Step 2:

[1737] The terminal packetizes the uploaded history data and sends it to the server. Specifically, a script on the terminal parses the history data and converts it into an appropriate format such as CSV or JSON. It then sends it to the server via API using the HTTPS protocol. The input is the converted history data, and the output is the data sent to the server.

[1738] Step 3:

[1739] The server analyzes the received history data and instructs the generative AI model to generate FAQs. Specifically, the server organizes the history data by theme and sends a request to the generative AI model along with a prompt such as "Generate an FAQ." The input is the organized history data and the prompt, and the output is a request sent to the generative AI model.

[1740] Step 4:

[1741] The generative AI model generates FAQs based on historical data. Specifically, the generative AI model analyzes historical data and automatically generates frequently asked questions and their answers. For example, a question and answer about the "warranty period of a product" is created. The input is historical data and prompt text, and the output is the generated FAQ.

[1742] Step 5:

[1743] The server sends the generated FAQ to the user's device. The generated FAQ is stored as a temporary file, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated FAQ, and the output is the notification sent to the user's device.

[1744] Step 6:

[1745] The user checks and edits the generated FAQ on the device. Specifically, the user checks the questions and answers in the FAQ editing interface displayed on the device, and makes corrections or additions as necessary. After making corrections, the user clicks the save button to confirm the final version. The input is clicking the link in the generated FAQ, and the output is the final, modified FAQ.

[1746] Training support system processing steps

[1747] Step 1:

[1748] Users use their devices to upload work procedures and workflows. Specifically, they select and upload procedures such as "Procedures for visiting customers" or "How to write sales reports" from the device's file upload dialog. The input is the procedure file, and the output is data stored on the device.

[1749] Step 2:

[1750] The terminal packetizes the uploaded business data and sends it to the server. Specifically, a script on the terminal analyzes the business data, converts it into an appropriate format, and sends it to the server using the HTTPS protocol. The input is the converted business data, and the output is the data sent to the server.

[1751] Step 3:

[1752] The server analyzes the received business data and instructs the generative AI model to generate training materials. Specifically, the server analyzes the procedure manual and business flow and sends a request to the generative AI model along with a prompt. The input is the procedure manual data and the prompt, and the output is a request sent to the generative AI model.

[1753] Step 4:

[1754] The generative AI model generates training materials based on the data it receives. Specifically, the generative AI model analyzes procedures and business flows, and automatically generates educational presentation materials and training manuals. For example, it creates materials such as "Points to remember when visiting customers" and "How to use sales report templates." The input is business data and prompts, and the output is the generated training materials.

[1755] Step 5:

[1756] The server sends the generated training materials to the user's device. The generated materials are stored as temporary files, linked to the user ID, and then notified to the user's device via a notification API. The input is the generated training materials, and the output is a notification of the sending to the user's device.

[1757] Step 6:

[1758] The user checks and provides the generated training materials on the terminal. Specifically, the user checks the training materials on the terminal, confirms that there are no problems, and then prepares to provide them to the new person in charge. The user reviews the materials and checks for errors or omissions. The input is clicking a link in the generated training materials, and the output is the final, confirmed training materials.

[1759] (Application example 1)

[1760] 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."

[1761] In modern manufacturing, rapid training and efficient implementation of new work procedures and workflows are key challenges. In particular, in-factory worker training often relies on manual explanations and training, which requires time and effort. Furthermore, there is a lack of technological means to efficiently manage work-related data and automatically generate handover and training materials based on that data. This creates a need for rapid and accurate training of new work procedures and workflows.

[1762] 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.

[1763] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and executing a generative model for automatically generating handover materials, means for outputting the generated handover materials, means for a factory worker to input or upload work procedures and workflows to a robot, means for the robot to send data to a central server, means for the central server to instruct the generative AI model to generate training materials, means for the generative AI model to generate training materials, means for the central server to send the generated training materials to the robot, and means for the robot to display the materials and for the worker to review and use. This enables factory workers to efficiently receive training on new work procedures and workflows.

[1764] "Business Data" refers to business-related information, documents, procedures, or business flows that are entered or uploaded by a user.

[1765] A "generative model" refers to an algorithm or machine learning model that analyzes data entered by a user and automatically generates materials and answers.

[1766] A "generative AI model" refers to an advanced model that uses artificial intelligence to analyze data entered by users and automatically generate materials.

[1767] "Handover materials" refers to documents that compile the information and procedures necessary to hand over work to another person in charge.

[1768] "Central Server" refers to a central computing device for remote data analysis and management.

[1769] "Training Materials" means educational documents and materials used to learn new business procedures and workflows.

[1770] A "business procedure manual" is a document that contains specific procedures and instructions for carrying out a specific task.

[1771] "Business flow" refers to a flowchart or diagram that shows the series of processes and steps by which a specific business is carried out.

[1772] A "robot" is an automated machine used in factories that has the ability to analyze data entered by workers and display materials.

[1773] A "display screen" refers to a display device installed on a robot or terminal, which visually displays generated materials and information.

[1774] "Factory workers" refer to employees who are engaged in tasks such as machine operation and assembly work within a factory.

[1775] This invention is a system that supports efficient training and job handover for factory workers. Specifically, the system allows users to provide work procedures and workflows, and a central server automatically generates training materials using a generative AI model based on these. The materials are then displayed by a robot.

[1776] Program processing explanation

[1777] Hardware and software used

[1778] Robot terminal: Robots used in factories are equipped with a display screen that provides an interface for workers to input work procedures and workflows.

[1779] Central server: A computing device for data management and processing that processes received business data and instructs the generative AI model on how to analyze it. Specific software includes a RESTful API for transferring data via API.

[1780] Generative AI models: Use advanced artificial intelligence models, such as OpenAI's GPT-4, to analyze the information you provide and automatically generate training and handover materials.

[1781] Specific program behavior

[1782] 1. Data entry: Factory workers use robotic terminals to input or upload new work procedures and workflows, such as "new assembly line procedures" or "machine maintenance workflows."

[1783] 2. Data transmission: The robot terminal transmits the entered or uploaded data to the central server. The data is transmitted using an HTTP POST request.

[1784] 3. Data analysis and material generation: The central server provides the received data to the generative AI model and instructs it to generate training materials. The generative AI model automatically generates training materials based on the data provided by the user.

[1785] 4. Material transmission and display: The generated training materials are sent back to the robot terminal by the central server, and the robot terminal displays the materials on the display screen for factory workers to review and use.

[1786] Adding specific examples

[1787] Prompt Sentence Examples

[1788] Please generate training materials based on the following operating procedures:

[1789] 1. Assembly line preparation

[1790] 2. Safety Check

[1791] 3. Machine start-up procedure

[1792] 4. Production line operation method

[1793] …

[1794] By providing the above prompts to the generative AI model, the model can analyze the user's work procedures and automatically generate relevant training materials, allowing factory workers to quickly master the new work procedures.

[1795] As described above, the present invention is a system that utilizes a generative AI model based on business data provided by the user to automatically generate and efficiently display business handover and training materials, thereby improving business efficiency and enabling rapid adaptation to new business operations.

[1796] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1797] Step 1:

[1798] Users use the robot terminal to input or upload work procedures and workflows. The input data includes specific details of the procedures and workflows, such as "new assembly line procedures" or "machine maintenance flow," in text or PDF format.

[1799] Step 2:

[1800] The robot terminal sends the input or uploaded business data to the central server. To do this, the robot terminal uses an HTTP POST request to send the data to the server via an API. The input data is packetized, converted into the appropriate format, and then sent.

[1801] Step 3:

[1802] The server provides the received data to the generative AI model and instructs it to generate training materials. The server sends the data to the generative AI model using an HTTP request, including a prompt such as "Please generate training materials based on the following business procedure manual."

[1803] Step 4:

[1804] The generative AI model analyzes the provided data and automatically generates training materials. The generative AI model creates specific training materials and manuals based on the work procedures entered by the user. For example, it generates materials including content such as "assembly line preparation," "safety checks," and "machine startup procedures."

[1805] Step 5:

[1806] The server sends the generated training materials to the robot terminal, converts the materials received from the generative AI model into an appropriate format, and sends them back to the robot terminal using an HTTP POST request.

[1807] Step 6:

[1808] The robot terminal displays the generated training materials on the display screen for the user to review and use. The robot terminal then displays the received materials on the display screen, allowing factory workers to review work procedures based on the materials and apply them to their actual work.

[1809] 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.

[1810] This invention analyzes business data entered by users, past email and chat history, business procedure manuals, and business flows, and automatically generates handover materials, frequently asked questions (FAQs), and training materials using a generative AI model and an emotion engine.By using the emotion engine, it is possible to analyze user emotions and reflect them in the generated materials.

[1811] An embodiment of a handover document creation system

[1812] Program processing explanation

[1813] 1. The user enters business data

[1814] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords and related documents such as "marketing strategy" and "calculation of advertising costs."

[1815] 2. Emotion engine recognizes emotions

[1816] The emotion engine recognizes the emotion of the user as they type. For example, the emotion engine analyzes the emotions (joy, anger, sadness, etc.) that the user is expressing while typing text.

[1817] 3. The device sends the data to the server

[1818] The device sends the input business data and the recognized emotion data to the server, packetizes the data, and sends a request to the server via API.

[1819] 4. The server instructs the generative AI model

[1820] The server analyzes the received business data and emotional data and issues instructions to the AI ​​model to generate documents. Specifically, it instructs the AI ​​model to "create handover documents," and also provides the emotional data to the AI ​​model.

[1821] 5. Generative AI model generates handover documents

[1822] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotional data. The AI ​​model adjusts the conversation scenario to reflect the emotional data, and converts it into a Markdown-formatted document to create the materials.

[1823] 6. The server sends the generated data to the terminal.

[1824] The server compiles the handover materials received from the generative AI model and sends them to the user's device, allowing the user to check the generated materials on their device.

[1825] 7. The user checks and edits the document

[1826] The user checks the handover materials generated on the terminal and edits and uses them as necessary, for example, by adding additional information to the materials or adjusting the layout.

[1827] FAQ creation system embodiment

[1828] 1. Users upload their past email and chat history

[1829] The user uses the device to upload past email data and chat history related to the work to be handed over. For example, the user uploads the past year's worth of customer support email history to the device.

[1830] 2. Emotion engine recognizes emotions

[1831] The emotion engine recognizes user emotions from email and chat history, for example, extracting emotions (e.g., customer dissatisfaction or satisfaction) within each email or chat.

[1832] 3. The device sends the history data to the server

[1833] Uploaded email and chat history data and recognized emotion data are sent from the device to the server, where the data is analyzed and converted into a data format suitable for sending to the server.

[1834] 4. The server instructs the generative AI model to generate FAQs

[1835] The server provides the received history data and emotion data to the generative AI model and instructs it to generate frequently asked questions and their answers. Specifically, it instructs the AI ​​model to "create FAQs" and provides the emotion data to the model.

[1836] 5. Generative AI model generates FAQs

[1837] The generative AI model analyzes the provided historical and sentiment data to automatically generate frequently asked questions and their answers. For example, based on customer sentiment analysis, it generates FAQs such as "About the product warranty period" and "How to process a refund."

[1838] 6. The server sends the generated FAQ to the device.

[1839] The server sends the generated FAQ and its answers to the user's terminal, where the data is repacketized and sent to the user's terminal.

[1840] 7. Users check and edit the FAQ

[1841] The user checks the FAQ generated on the terminal, makes corrections or additions as necessary, and provides it to the new support staff.

[1842] Embodiment of training support system

[1843] 1. The user inputs or uploads a business procedure manual or workflow.

[1844] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, the user can upload procedure manuals such as "procedures for visiting customers" and "how to write sales reports."

[1845] 2. Emotion engine recognizes emotions

[1846] The emotion engine recognizes the user's emotions in the procedure manual or workflow. For example, it analyzes the emotions (satisfaction, anxiety, etc.) when creating the procedure manual.

[1847] 3. The device sends business data to the server

[1848] The uploaded business data and recognized emotion data are sent from the device to the server, which then packets the data and sends it to the server.

[1849] 4. The server instructs the AI ​​model to generate training materials.

[1850] The server provides the received business data and emotional data to the generative AI model and instructs it to generate training materials. Specifically, it instructs the AI ​​model to "create training materials" and provides the emotional data to the model.

[1851] 5. Generative AI models generate training materials

[1852] The generative AI model analyzes business data and emotional data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[1853] 6. The server sends the generated training materials to the device.

[1854] The server compiles the generated training materials and sends them to the user's device, where they can review the materials.

[1855] 7. User confirms and provides materials

[1856] The user checks the training materials generated on the terminal, checks the contents as necessary, and then provides the materials to the new person in charge.

[1857] As described above, this invention utilizes a generative AI model and an emotion engine to automatically generate materials based on data and emotion data entered by the user, and provides the results to the user, thereby streamlining business handover and enabling rapid adaptation to new business operations.

[1858] The processing flow will be explained below.

[1859] Processing steps of the handover document creation system

[1860] Creating handover documents

[1861] Step 1:

[1862] The user uses a terminal to input or upload keywords and related documents related to the business to be handed over. For example, the user inputs keywords such as "marketing strategy" and "calculation of advertising costs."

[1863] Step 2:

[1864] The emotion engine recognizes the user's emotions. The emotion engine analyzes emotions such as "stress" and "expectation" from the text entered by the user and the contents of related documents.

[1865] Step 3:

[1866] The device sends the input business data and emotion data to the server. Specifically, the encoded data is packetized and sent to the server via an API.

[1867] Step 4:

[1868] The server provides the received business data and emotion data to the generative AI model and instructs it to generate documents. The server then sends the data to the AI ​​model along with the instruction to "create handover documents."

[1869] Step 5:

[1870] The generative AI model automatically generates conversation content and materials for handovers based on business data and emotion data. The materials are created in Markdown format and include explanations and points of caution that reflect the emotion data.

[1871] Step 6:

[1872] The server sends the generated handover materials to the terminal. The server compiles the materials, packets them into a format that is easy for the user to understand, and sends them to the terminal.

[1873] Step 7:

[1874] The user checks the generated handover documents on the terminal and edits them as necessary, for example, by correcting the text of the documents or adding additional information.

[1875] FAQ creation

[1876] Step 1:

[1877] The user uses a device to upload past email data and chat history related to the work being handed over. For example, upload the past year's worth of customer support email history.

[1878] Step 2:

[1879] The emotion engine recognizes user emotions from email and chat history, extracting emotions such as "satisfied" or "dissatisfied" from customer interactions.

[1880] Step 3:

[1881] The device sends the uploaded history data and emotion data to the server, which analyzes the encoded data, converts it into a format suitable for transmission, and sends it to the server.

[1882] Step 4:

[1883] The server instructs the generation AI model to generate an FAQ. The server instructs the AI ​​model to "create an FAQ" and provides historical data and emotional data to the AI ​​model.

[1884] Step 5:

[1885] Generative AI models analyze historical and sentiment data to automatically generate frequently asked questions (FAQs) and their answers, for example, by taking customer sentiment into account.

[1886] Step 6:

[1887] The server sends the generated FAQ to the terminal. The server compiles the information, packets it into an appropriate format, and sends it to the user's terminal.

[1888] Step 7:

[1889] The user checks the generated FAQ on the device and makes corrections or additions as necessary, for example, adding new questions or making changes to clarify the answers.

[1890] Training Support

[1891] Step 1:

[1892] Using a terminal, the user inputs or uploads in PDF format the procedure manuals and workflow diagrams related to the tasks being trained. For example, they can upload procedure manuals such as "procedures for visiting customers" or "how to write sales reports."

[1893] Step 2:

[1894] The emotion engine recognizes the user's emotions from the procedure manual and business flow, analyzing emotions such as "sense of relief" and "confusion" from the contents and explanations of the procedure manual.

[1895] Step 3:

[1896] The device sends business data and emotion data to the server, which encodes the data into packets and sends them to the server via API.

[1897] Step 4:

[1898] The server instructs the AI ​​model to generate training materials. The server instructs the AI ​​model to "create training materials" and provides data and emotional information to the AI ​​model.

[1899] Step 5:

[1900] The generative AI model analyzes business data and emotion data to generate educational presentation materials and training manuals, such as "Key points to remember when visiting customers" and "How to use sales report templates."

[1901] Step 6:

[1902] The server sends the generated training materials to the terminal, where it compiles the materials, packets them into an appropriate format for delivery to the user, and transmits them.

[1903] Step 7:

[1904] The user checks the training materials generated on the terminal, and after reviewing the content as necessary, provides the materials to the new personnel, for example by editing the materials to add additional explanations.

[1905] Through this process, handover materials, FAQs, and training materials that reflect the user's feelings are automatically generated. This system makes the handover process more efficient and enables rapid business adaptation.

[1906] Example 2

[1907] 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."

[1908] In today's business environment, the creation of efficient handover documents, frequently asked questions (FAQs), and training materials is a key challenge. However, creating these documents manually requires a significant amount of time and effort, and often loses emotional nuance and context. It is also difficult to effectively utilize past communications and operating procedures. Therefore, there is a need for automated, efficient, and emotionally sensitive document generation.

[1909] 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.

[1910] In this invention, the server includes means for receiving data related to work input by a user, means for analyzing the data and recognizing emotions, means for transmitting the input data and emotional data from the user terminal to the server, means for automatically generating handover materials, FAQs, and training materials using a generative AI model based on the received data and emotional data, and means for transmitting and outputting the generated materials to the user terminal. This improves the efficiency of automatic generation of work-related materials and enables the creation of more appropriate materials that take user emotions into consideration.

[1911] "Data related to business" refers to information related to business input by a user, and includes all data necessary for business execution, including text, documents, keywords, and the like.

[1912] An "emotion engine" is an algorithm that analyzes text and data entered by users and recognizes and classifies emotions.

[1913] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning techniques to automatically generate specific materials from input data.

[1914] "Handover materials" are documents and guides created to efficiently ...

Claims

1. means for receiving transaction-related data input by a user; means for analyzing the data and executing a generative model for automatically generating handover materials; A means for outputting the generated handover materials; A system including:

2. means for receiving past email and chat history entered by the user; means for analyzing the received historical data and executing a generative model for automatically generating frequently asked questions and their answers; A means to output the generated frequently asked questions and their answers; The system of claim 1 , comprising:

3. A means for receiving a work procedure manual or a work flow input by a user; means for analyzing the received data and executing a generative model to automatically generate training material; means for outputting the generated training materials; The system of claim 1 , comprising:

4. means for transmitting user input data to a server for analysis and generation; means for transmitting the material generated by the generative model to a terminal for providing to a user; 4. The system of claim 1, 2 or 3, comprising:

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