system

Abstract

We provide the system. [Solution] A means for automatically collecting business documents from information media that store such documents, A means for extracting important information from the aforementioned collected materials and automatically generating business handover documents, A means of automatically generating frequently asked questions and their answers by analyzing past dialogue records using a conversation generation model, A means of providing interactive training to new employees and enabling them to learn work procedures, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The significant time and effort involved in business handover pose a major burden on companies during personnel transfers or retirements. Existing methods require manual document creation and preparation for conversations, which reduces the efficiency of the handover process. There is also a problem that it is difficult to provide appropriate training for the new person in charge to become familiar with the business in a short period of time after the handover. An effective system for efficiently solving these problems is required.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects and extracts necessary documents from information media storing business data and automatically generates business handover documents based on important information. Furthermore, it analyzes past dialogue records to generate frequently asked questions and corresponding answers, automating FAQs during handover. This creates a system that enables interactive training for new business personnel. Through these means, the time and effort required for business handover can be significantly reduced, and a smooth transition of operations can be supported.

[0006] "Business documents" refer to all documents and records created or used by a company or organization in the course of carrying out its business operations.

[0007] "Information media" refers to systems or devices for storing or transferring information in digital or analog format.

[0008] "Automatic collection methods" refer to the function of a system that acquires data and information based on pre-set conditions without human intervention.

[0009] "Means for extracting important information" refers to the function of analyzing and identifying items and content essential to business operations from collected data.

[0010] A "business handover document" refers to a document that systematically organizes the information necessary for a new person to take over a business.

[0011] A "conversation generation model" refers to an algorithm or software that learns from past dialogue history and patterns as data to generate new conversation content.

[0012] "Frequently Asked Questions" refers to the types of inquiries that are frequently received regarding business operations and procedures.

[0013] "Means of providing interactive training" refers to system functions that provide an environment in which users can acquire skills and knowledge while exchanging information in a two-way manner. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The business handover support system according to the present invention forms a platform for effectively collecting and generating information between servers, terminals, and users. This system primarily satisfies the following requirements.

[0036] First, the server automatically collects business documents from cloud storage and internal servers as information sources. During this process, it analyzes metadata and keywords within documents to select business-related materials. For example, it can retrieve the latest project reports through the API of a project management tool.

[0037] Next, the server extracts important information from the collected data and generates a handover document using natural language processing technology. This process maintains information consistency by using standardized templates. Furthermore, the generated document clearly explains the background and procedures of the work and is provided to the user.

[0038] Furthermore, the server utilizes a conversation generation model to automatically generate frequently asked questions and their answers from past conversation records. This allows users to quickly resolve questions in their daily work. For example, in customer support operations, a list of FAQs based on past inquiries is published in real time, allowing new support staff to refer to it immediately.

[0039] Furthermore, this system includes means of providing interactive training. Users can learn interactively through a terminal, and the training modules are designed to align with work procedures. This allows new employees to improve their skills through training that simulates real-world work scenarios.

[0040] Thus, the present invention provides a system that reduces the time and effort required for business handover and supports efficient business transitions. Depending on the embodiment, the system can be flexibly customized and adapted to a variety of business scenarios.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server periodically scans the information media where business documents are stored. During this scan, it filters business-related files based on specific metadata and keywords, and retrieves the data via download or streaming.

[0044] Step 2:

[0045] Based on the data acquired by the server, natural language processing technology is used to analyze important sections and sentences. This analysis extracts the information necessary for handing over duties and automatically generates documents according to a template.

[0046] Step 3:

[0047] The server formats the generated handover documents and performs summarization as needed. As a result, the documents are optimized into a format that is easy for users to understand.

[0048] Step 4:

[0049] The server analyzes past email and chat history and uses a conversation generation model to automatically generate frequently asked questions and their answers. This process identifies key inquiry patterns based on frequency analysis.

[0050] Step 5:

[0051] The terminal displays generated FAQs and conversation modules on its interface, providing users with a state where inquiries can be handled in real time.

[0052] Step 6:

[0053] Users initiate interactive training through their devices and learn work procedures using training modules generated by the server. During this process, the user's progress is also recorded on the server via their devices.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] The challenges lie in streamlining information sharing during business handover and ensuring that new employees quickly acquire the necessary skills. In particular, the lack of a system for organizing necessary information from a large volume of documents and resolving questions by utilizing past conversation records may slow down the transition process.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for automatically collecting business information from an information storage device, means for analyzing important information from the collected information and automatically generating business transition documents, and means for analyzing previous dialogue history using a conversation generation algorithm and automatically generating frequently asked questions and their answers. This makes it possible for new business personnel to quickly and efficiently obtain the necessary information and understand the business, significantly reducing the time and effort required during business transition.

[0059] An "information storage device" is a device for storing and managing digital data and information, and includes cloud storage and internal servers.

[0060] "Business information" refers to documents, data, reports, and other related materials that are associated with business activities and enable the performance of those activities.

[0061] "Methods for automatic collection" refers to a system that uses specific algorithms or programs to acquire necessary business information from information storage devices without human intervention.

[0062] "Methods for analyzing and automatically generating business transition documents" refers to methods that use natural language processing technology and algorithms to analyze collected information and automatically create documents based on that information.

[0063] A "conversation generation algorithm" is a computational procedure or process that analyzes past dialogues and document data to generate frequently asked questions and appropriate responses.

[0064] "Previous dialogue history" refers to records and logs of past communications, and this data is used to leverage past insights by analyzing it.

[0065] "Methods of automatic generation" refer to methods of processing data under specific conditions based on a program to produce results.

[0066] The business handover support system according to the present invention has a structure in which a server, terminal, and user work together.

[0067] The server accesses information storage devices and automatically collects business information. Using cloud storage or internal servers, it retrieves information through specific algorithms and stores it in digital format. The server can then analyze the information using natural language processing techniques. Specifically, it processes text using libraries such as "Python's NLTK" and "spaCy" to extract important information necessary for business migration.

[0068] Based on the analyzed information, the server generates business migration documents. This is done using standardized templates, automatically creating consistent documents that reduce human error and improve operational efficiency.

[0069] Furthermore, the server uses a conversation generation algorithm to analyze previous conversation history and generate FAQs (Frequently Asked Questions). This allows users to quickly obtain answers to frequently asked questions. The server utilizes a "generative AI model" to build FAQs that include responses in natural language.

[0070] Users can use their own devices to access the provided business migration documents and FAQs to deepen their understanding of their work. Through this system, users can check the workflow and quickly obtain the information they need for their work.

[0071] As a concrete example, here are some examples of prompt statements for a generative AI model:

[0072] "Please extract the key points from the latest report on the ongoing project and create a clear and concise summary for the new person in charge."

[0073] Such prompts allow the server to quickly process the necessary information and present it to the user. This enables the user to review documents without wasting time and to quickly take over tasks or start new work.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects business information from information storage devices. Inputs include documents stored in cloud storage or on internal servers. The server automatically accesses these documents using an API and downloads the necessary files. The output is a set of collected digital documents.

[0077] Step 2:

[0078] The server analyzes the collected documents using natural language processing techniques. The input is the documents collected in step 1. The server uses libraries such as "Python's NLTK" and "spaCy" to perform text mining on these documents and extract important information and keywords. The output is a list of the extracted important information.

[0079] Step 3:

[0080] The server generates business migration documents based on the extracted information. The input is a list of important information obtained in Step 2. The server uses a standardized template to automatically generate documents while maintaining information consistency. The output is a consistent business migration document.

[0081] Step 4:

[0082] The server generates FAQs using a conversation generation algorithm. The input is past conversation history data. The server analyzes the past conversation data and automatically generates frequently asked questions and their answers. The output is an FAQ list.

[0083] Step 5:

[0084] Users review the generated business migration documents and FAQs via their terminals and utilize them in their work. The input consists of business migration documents and FAQs provided by the server. Users access the materials via their terminals, understand the necessary information, and proceed with their work. The output is improved user understanding and work efficiency.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In modern industry, the handover of tasks is a time-consuming and labor-intensive challenge. Especially in manufacturing, where diverse procedures and rules exist, it is difficult for new workers to adapt immediately. Furthermore, failure to efficiently transfer the knowledge of experienced workers can lead to a decline in quality and production efficiency. Therefore, there is a need for new systems that enable effective task handover and support the skill development of workers.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for automatically collecting business data from an information medium storing such data, means for extracting important information from the collected data and automatically generating business handover documents, means for analyzing past dialogue records using dialogue generation technology and automatically generating frequently asked questions and their answers, and display and audio guide devices for visually and audibly presenting business procedures and answers to questions. As a result, new employees can proceed with their work while receiving accurate information in real time, thereby improving overall business efficiency.

[0090] "Business documents" refer to a collection of documents and data used by companies and organizations for their daily operations and business execution.

[0091] An "information medium" is a physical or digital platform for storing or transferring data or documents.

[0092] "Means" refers to the methods or technical measures used to achieve a specific objective.

[0093] The term "device" refers to a machine or system designed to perform a specific function.

[0094] "Dialogue generation technology" is a technology that automatically generates dialogue in text or voice using natural language processing.

[0095] "Interactive training" is a learning format in which users actively participate and progress while receiving feedback.

[0096] "Information equipment" refers to electronic devices and digital tools used for data input, processing, and output.

[0097] "Presenting visually and aurally" refers to communicating information to users using displays and sound.

[0098] This invention is a system for facilitating the smooth handover of business operations, in which a server, terminal, and user work together to effectively realize its functions. Specific embodiments are described below.

[0099] The server first automatically collects business documents from various information sources. This process involves retrieving data from cloud storage and internal servers, and extracting key information using a natural language processing engine. This enables the automatic generation of business handover documents in a standardized template format. These generated documents are then visually displayed to aid understanding by the new business 담당자 (person in charge).

[0100] Furthermore, the server uses dialogue generation technology to analyze past dialogue records. This allows it to provide frequently asked questions and their answers in real time. This information is transmitted to user-operated information devices, such as smart glasses worn by factory workers. The smart glasses present the information visually and also serve as audio guides. This allows workers to efficiently access information without taking their hands off the device.

[0101] This system also provides interactive training. Through a terminal, users receive training that simulates work scenarios, allowing them to learn actual work procedures. For example, when a new staff member performs machine maintenance for the first time, a list of necessary tools and detailed instructions for each step are displayed on smart glasses, and voice guidance is played in the user's ear.

[0102] A concrete example of a prompt is as follows: "Generative AI model, summarize the routine maintenance procedure for the factory's shredder machine, along with the key points, and present it in a visually understandable format." By using prompts like this, even complex business procedures can be presented in an easy-to-understand manner.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server automatically collects business documents from various information sources. Inputs include connection information for cloud storage and internal servers, while output is a dataset of business documents. The server accesses these data sources via APIs, uses metadata and keywords to select relevant documents, and stores them in a database.

[0106] Step 2:

[0107] The server extracts important information from the collected materials and generates a business handover document. The input is a dataset of business materials collected in step 1, and the output is a business handover document in a standardized format. The server uses a natural language processing engine to parse the text and format the document according to a predetermined template.

[0108] Step 3:

[0109] The server uses dialogue generation technology to analyze past dialogue records and generate frequently asked questions and their answers. The input is past dialogue record data, and the output is an FAQ list. The server uses a generation AI model to recognize patterns in past records, extract frequently asked questions, and generate corresponding answers in natural language.

[0110] Step 4:

[0111] The server transmits the generated handover documents and FAQs to the information device. The input is a list of handover documents and FAQs, and the output is display data provided to the information device. The server transmits this data to the terminal via the network, making it easy for users to obtain the information visually and aurally.

[0112] Step 5:

[0113] The device provides users with interactive training. Input consists of display and audio data received from the server, while output is feedback on the user's understanding and skill improvement. The device displays work procedures and answers through the smart glasses' display and speaker, and tracks user actions to record progress.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention incorporates an emotion engine into a business handover support system to provide information and training that takes into account the user's emotional state. This system includes a mechanism for effectively exchanging information between the server, terminal, and user.

[0116] The server first automatically collects business documents from information media, extracts important information using natural language processing technology as needed, and generates business handover documents. During this process, an emotion engine considers the user's past response data and appropriately adjusts the tone and content of the information provided. For example, if stress is detected, the document can be restructured to be concise and easy to understand.

[0117] Furthermore, the server utilizes a conversation generation model to generate personalized responses in real time based on user inquiries. The emotion engine analyzes the user's tone of voice and text during the conversation and selects a dialogue style appropriate for the user. For example, if the user is showing signs of anxiety, it can provide more supportive answers or additional information.

[0118] Users can participate in interactive training delivered via their devices, with the training content individually tailored by an emotion engine. This tailoring includes learning pace and feedback methods. To enhance user satisfaction, the system also includes a feature that provides timely motivational content based on progress.

[0119] Thus, by utilizing an emotion engine, this invention enables flexible job handover and training based on the user's emotional state, supporting an efficient and smooth transition of operations. This makes it possible to improve the user experience and operational efficiency.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The server periodically scans information media and automatically collects business-related documents. This collection process uses metadata and keywords to select the most relevant files.

[0123] Step 2:

[0124] The server uses collected data to apply natural language processing techniques and extract important information. During this process, an emotion engine analyzes past user response data to adjust the tone and content of the document.

[0125] Step 3:

[0126] The server-generated handover documents are formatted and prepared for provision to the user. The formatted documents are summarized as needed and presented in a user-friendly format.

[0127] Step 4:

[0128] The conversation generation model is activated by the server and generates frequently asked questions and their answers from past dialogue records and current user input data. In this step, the emotion engine is used to determine the user's emotional state and optimize the dialogue style.

[0129] Step 5:

[0130] The device displays generated FAQs and interactive training modules to the user. The emotion engine analyzes the user's responses in real time and dynamically adjusts the training content as needed.

[0131] Step 6:

[0132] Users receive training through their devices and appropriate feedback provided by an emotion engine. This feedback includes motivational content that takes into account the user's learning pace and emotional state.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] During the handover of work responsibilities, tasks such as collecting documents, extracting important information, and providing individualized support that takes emotions into consideration are required, but performing these tasks manually is extremely time-consuming and laborious. Furthermore, there is a lack of systems that provide interactive training adapted to each user's learning pace and emotional state, and that maintain and improve motivation.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for automatically collecting documents from information media, means for automatically generating handover documents by analyzing important data, and means for analyzing emotional states and adjusting the tone and content of the data. This improves the efficiency of handover and enables the provision of individually optimized information and training to users.

[0138] An "information medium" is a device or system for storing and managing information in digital or physical form.

[0139] "Automated generation" refers to a process in which machines or software process data to create new documents or responses with minimal human intervention.

[0140] A "generative model" is an algorithm or artificial intelligence framework that learns from existing data and creates new information or responses.

[0141] "Interactive learning" is an educational process that transmits information through dialogue with the user and supports learning in a two-way manner.

[0142] "Emotional state" refers to the psychological or emotional state a user experiences, and is often a factor that influences their behavior and reactions.

[0143] "Motivation-enhancing content" refers to information and activities provided to stimulate users' motivation and interest.

[0144] This invention provides a system that streamlines job handover and training, and at its core is a structure in which a server and terminals work in coordination with each other.

[0145] The server automatically collects business documents from information media and extracts important information using natural language processing technology. The software used is a specialized text analysis engine designed to process large amounts of text data quickly.

[0146] Furthermore, the server is equipped with a generative AI model that analyzes the user's past conversation history to automatically generate frequently asked questions and their answers. In this process, an emotion engine analyzes the user's tone of voice and written text, adjusting the tone and content of the response to suit the user. For example, a user who is anxious about operating a new system will be given an explanation in a supportive tone. For instance, a prompt such as, "Please give advice to a novice user who is feeling stressed about the setup procedure for the new software," might be used.

[0147] The device provides users with personalized, interactive training. This involves displaying pre-configured learning materials sent from a server on the user's device, progressing at the user's pace. This interactive training incorporates assessments and feedback provided by an emotional engine to provide more appropriate guidance.

[0148] Furthermore, the device monitors the user's learning progress and provides motivational content based on that progress. For example, it displays a message of praise to users who complete a specific section and suggests new challenges to encourage further skill acquisition.

[0149] These features enable efficient handover of tasks and adaptive learning by users.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The server collects business data from information media. These information media include document management systems and mail servers. It searches for and collects relevant files and messages from these media. It obtains identifiers for business-related information as input and the collected raw data as output.

[0153] Step 2:

[0154] The server analyzes the collected data using natural language processing technology to extract important information. Specifically, a text analysis engine analyzes keywords and grammatical structures to identify key points of the business. It analyzes raw data as input and generates extracted important data as output.

[0155] Step 3:

[0156] The server uses an emotion engine to analyze the user's past emotional data and adjust the tone and content of important data. For example, if the user has shown stress in the past, the data will be simplified. The server uses the user's emotional data as input and generates an adjusted handover document as output.

[0157] Step 4:

[0158] The server utilizes a generative AI model to generate responses to user inquiries. It analyzes past dialogue history and creates the optimal response using the generative model. It uses real-time user input and dialogue history as input and provides individual responses as output.

[0159] Step 5:

[0160] The device provides users with interactive training. It displays learning materials according to the user's learning pace and supports their progress. It takes training content sent from the server as input and outputs training at a pace tailored to the user.

[0161] Step 6:

[0162] The server and terminal evaluate the user's training progress and provide motivational content. Once progress is confirmed, they present congratulatory messages and new challenges. As input, they analyze user progress data, and as output, they generate appropriate content.

[0163] (Application Example 2)

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

[0165] Traditional job handover and training systems do not provide flexible information that takes into account the emotional state of individual users. Therefore, stress and anxiety are not mitigated during the process of new employees or operators smoothly understanding and learning work procedures, potentially impacting work efficiency and productivity. Consequently, there is a need to personalize information provision and training based on users' emotions to ensure a smooth transition of work.

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

[0167] In this invention, the server includes a function to automatically collect information from a device that stores information related to business operations; a function to analyze important elements from the collected information and automatically generate business transition documents; a function to analyze past interaction records using a conversation generation algorithm and automatically generate typical questions and their answers; and a function to analyze the user's emotional state and adjust the content and tone of the instructions provided. This enables flexible and effective business handover and training tailored to the individual user's emotions.

[0168] "Information related to business operations" refers to data and knowledge necessary for business operations and the implementation of procedures.

[0169] "Device" refers to a component of hardware or software used to store information.

[0170] An "automatic data collection function" is a function that has a process for acquiring information without manual intervention, based on pre-programmed procedures.

[0171] A "function for analyzing important elements" refers to a function that has the process of identifying and extracting business-useful and essential information from a large amount of data.

[0172] A "business process transition document" is a document that organizes and describes the necessary procedures and knowledge to facilitate the transfer of information to new business personnel.

[0173] A "conversation generation algorithm" is a computational method that uses natural language processing technology to mimic human dialogue and construct rational responses.

[0174] "Interaction records" refer to the history of past conversations and communications, and are used as data for improvement and learning.

[0175] "Typical questions and answers" are intended to provide efficient information by listing frequently asked questions and their appropriate corresponding answers.

[0176] The "function that analyzes the user's emotional state and adjusts the content and tone of the instructions provided" is a function that judges the user's psychological state and dynamically optimizes the method of providing appropriate information accordingly.

[0177] This invention is a system that enables smooth job handover and training. The system automatically collects necessary data from a device that stores job-related information, analyzes important information based on this data, and creates job transition documents. Furthermore, the system uses a conversation generation algorithm to analyze past interaction records and dynamically generate typical questions and their answers. It also analyzes the user's emotional state and appropriately adjusts the content and tone of instructions to provide optimal information.

[0178] The server uses natural language processing libraries and sentiment analysis engines to analyze the tone of the user's voice and text in real time. Based on this analysis, it provides information that takes into account the user's mental state, supporting the process of learning new tasks. Specifically, sentiment analysis tools such as IBM Watson® and AWS® Comprehend are used. Furthermore, OpenAI® GPT-3® is used as a generative AI model to provide sophisticated conversational responses.

[0179] Users interact with this system via their terminals and receive information about their work in an easily understandable format. For example, when a new factory operator is undergoing training, the system not only explains the operating procedures in detail but also provides encouragement and supplementary information when it senses the operator's anxiety. This allows operators to work with greater confidence.

[0180] An example of a prompt sentence to be input into the generating AI model is, "Please provide a gentle way to instruct an operator who is feeling stressed on the production line." In this way, the present invention leverages its technological advancements to contribute to increased operational efficiency and improved user learning processes.

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

[0182] Step 1:

[0183] The server automatically collects data from devices that store information related to business operations. The input at this stage is raw data stored in the information storage device, and the output is organized business-related data. The server periodically monitors the devices and automatically retrieves new data when available.

[0184] Step 2:

[0185] The server analyzes key elements from the collected data and automatically generates business migration documents. The organized business-related data obtained in Step 1 is used as input. Natural language processing libraries (e.g., SpaCy or NLTK) are used to identify important information, and the output is a business migration document in a format that is easy for new business personnel to understand.

[0186] Step 3:

[0187] The server analyzes past interaction records using a conversation generation algorithm. The input is the collected history of past conversations. Based on this history, it generates typical questions and corresponding answers, automatically creating a list of frequently asked questions as output. By using a generation AI model (e.g., GPT-3), the high quality of the generated responses is guaranteed.

[0188] Step 4:

[0189] The server analyzes the user's emotional state and adjusts the content and tone of the information provided in real time. Input is user voice and text data, and the system uses an emotion analysis engine (e.g., IBM Watson, AWS Comprehend) to evaluate the user's emotions. The output generates flexible instructions tailored to the user's emotional state. For example, if the user is stressed, the system provides simpler and easier-to-understand instructions.

[0190] Step 5:

[0191] Users access business migration documents and FAQs generated through their terminals and receive interactive training. Input is learning materials provided by the server, and output is the user's learning progress and feedback. Motivational content and supplementary information are also provided to address any anxieties or questions users may have during their learning process.

[0192] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0198] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0199] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0200] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0202] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0203] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0204] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0206] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0207] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0208] The business handover support system according to the present invention forms a platform for effectively collecting and generating information between servers, terminals, and users. This system primarily satisfies the following requirements.

[0209] First, the server automatically collects business documents from cloud storage and internal servers as information sources. During this process, it analyzes metadata and keywords within documents to select business-related materials. For example, it can retrieve the latest project reports through the API of a project management tool.

[0210] Next, the server extracts important information from the collected data and generates a handover document using natural language processing technology. This process maintains information consistency by using standardized templates. Furthermore, the generated document clearly explains the background and procedures of the work and is provided to the user.

[0211] Furthermore, the server utilizes a conversation generation model to automatically generate frequently asked questions and their answers from past conversation records. This allows users to quickly resolve questions in their daily work. For example, in customer support operations, a list of FAQs based on past inquiries is published in real time, allowing new support staff to refer to it immediately.

[0212] Furthermore, this system includes means of providing interactive training. Users can learn interactively through a terminal, and the training modules are designed to align with work procedures. This allows new employees to improve their skills through training that simulates real-world work scenarios.

[0213] Thus, the present invention provides a system that reduces the time and effort required for business handover and supports efficient business transitions. Depending on the embodiment, the system can be flexibly customized and adapted to a variety of business scenarios.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The server periodically scans the information media where business documents are stored. During this scan, it filters business-related files based on specific metadata and keywords, and retrieves the data via download or streaming.

[0217] Step 2:

[0218] Based on the data acquired by the server, natural language processing technology is used to analyze important sections and sentences. This analysis extracts the information necessary for handing over duties and automatically generates documents according to a template.

[0219] Step 3:

[0220] The server formats the generated handover documents and performs summarization as needed. As a result, the documents are optimized into a format that is easy for users to understand.

[0221] Step 4:

[0222] The server analyzes past email and chat history and uses a conversation generation model to automatically generate frequently asked questions and their answers. This process identifies key inquiry patterns based on frequency analysis.

[0223] Step 5:

[0224] The terminal displays generated FAQs and conversation modules on its interface, providing users with a state where inquiries can be handled in real time.

[0225] Step 6:

[0226] Users initiate interactive training through their devices and learn work procedures using training modules generated by the server. During this process, the user's progress is also recorded on the server via their devices.

[0227] (Example 1)

[0228] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0229] The challenges lie in streamlining information sharing during business handover and ensuring that new employees quickly acquire the necessary skills. In particular, the lack of a system for organizing necessary information from a large volume of documents and resolving questions by utilizing past conversation records may slow down the transition process.

[0230] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0231] In this invention, the server includes means for automatically collecting business information from an information storage device, means for analyzing important information from the collected information and automatically generating business transition documents, and means for analyzing previous dialogue history using a conversation generation algorithm and automatically generating frequently asked questions and their answers. This makes it possible for new business personnel to quickly and efficiently obtain the necessary information and understand the business, significantly reducing the time and effort required during business transition.

[0232] An "information storage device" is a device for storing and managing digital data and information, and includes cloud storage and internal servers.

[0233] "Business information" refers to documents, data, reports, and other related materials that are associated with business activities and enable the performance of those activities.

[0234] "Methods for automatic collection" refers to a system that uses specific algorithms or programs to acquire necessary business information from information storage devices without human intervention.

[0235] "Methods for analyzing and automatically generating business transition documents" refers to methods that use natural language processing technology and algorithms to analyze collected information and automatically create documents based on that information.

[0236] A "conversation generation algorithm" is a computational procedure or process that analyzes past dialogues and document data to generate frequently asked questions and appropriate responses.

[0237] "Previous dialogue history" refers to records and logs of past communications, and this data is used to leverage past insights by analyzing it.

[0238] "Methods of automatic generation" refer to methods of processing data under specific conditions based on a program to produce results.

[0239] The business handover support system according to the present invention has a structure in which a server, terminal, and user work together.

[0240] The server accesses information storage devices and automatically collects business information. Using cloud storage or internal servers, it retrieves information through specific algorithms and stores it in digital format. The server can then analyze the information using natural language processing techniques. Specifically, it processes text using libraries such as "Python's NLTK" and "spaCy" to extract important information necessary for business migration.

[0241] Based on the analyzed information, the server generates business migration documents. This is done using standardized templates, automatically creating consistent documents that reduce human error and improve operational efficiency.

[0242] Furthermore, the server uses a conversation generation algorithm to analyze previous conversation history and generate FAQs (Frequently Asked Questions). This allows users to quickly obtain answers to frequently asked questions. The server utilizes a "generative AI model" to build FAQs that include responses in natural language.

[0243] Users can use their own devices to access the provided business migration documents and FAQs to deepen their understanding of their work. Through this system, users can check the workflow and quickly obtain the information they need for their work.

[0244] As a concrete example, here are some examples of prompt statements for a generative AI model:

[0245] "Please extract the key points from the latest report on the ongoing project and create a clear and concise summary for the new person in charge."

[0246] Such prompts allow the server to quickly process the necessary information and present it to the user. This enables the user to review documents without wasting time and to quickly take over tasks or start new work.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The server collects business information from information storage devices. Inputs include documents stored in cloud storage or on internal servers. The server automatically accesses these documents using an API and downloads the necessary files. The output is a set of collected digital documents.

[0250] Step 2:

[0251] The server analyzes the collected documents using natural language processing techniques. The input is the documents collected in step 1. The server uses libraries such as "Python's NLTK" and "spaCy" to perform text mining on these documents and extract important information and keywords. The output is a list of the extracted important information.

[0252] Step 3:

[0253] The server generates business migration documents based on the extracted information. The input is a list of important information obtained in Step 2. The server uses a standardized template to automatically generate documents while maintaining information consistency. The output is a consistent business migration document.

[0254] Step 4:

[0255] The server generates FAQs using a conversation generation algorithm. The input is past conversation history data. The server analyzes the past conversation data and automatically generates frequently asked questions and their answers. The output is an FAQ list.

[0256] Step 5:

[0257] Users review the generated business migration documents and FAQs via their terminals and utilize them in their work. The input consists of business migration documents and FAQs provided by the server. Users access the materials via their terminals, understand the necessary information, and proceed with their work. The output is improved user understanding and work efficiency.

[0258] (Application Example 1)

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

[0260] In modern industry, the handover of tasks is a time-consuming and labor-intensive challenge. Especially in manufacturing, where diverse procedures and rules exist, it is difficult for new workers to adapt immediately. Furthermore, failure to efficiently transfer the knowledge of experienced workers can lead to a decline in quality and production efficiency. Therefore, there is a need for new systems that enable effective task handover and support the skill development of workers.

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

[0262] In this invention, the server includes means for automatically collecting business data from an information medium storing such data, means for extracting important information from the collected data and automatically generating business handover documents, means for analyzing past dialogue records using dialogue generation technology and automatically generating frequently asked questions and their answers, and display and audio guide devices for visually and audibly presenting business procedures and answers to questions. As a result, new employees can proceed with their work while receiving accurate information in real time, thereby improving overall business efficiency.

[0263] "Business documents" refer to a collection of documents and data used by companies and organizations for their daily operations and business execution.

[0264] An "information medium" is a physical or digital platform for storing or transferring data or documents.

[0265] "Means" refers to the methods or technical measures used to achieve a specific objective.

[0266] The term "device" refers to a machine or system designed to perform a specific function.

[0267] "Dialogue generation technology" is a technology that automatically generates dialogue in text or voice using natural language processing.

[0268] "Interactive training" is a learning format in which users actively participate and progress while receiving feedback.

[0269] "Information equipment" refers to electronic devices and digital tools used for data input, processing, and output.

[0270] "Presenting visually and aurally" refers to communicating information to users using displays and sound.

[0271] This invention is a system for facilitating the smooth handover of business operations, in which a server, terminal, and user work together to effectively realize its functions. Specific embodiments are described below.

[0272] The server first automatically collects business documents from various information sources. This process involves retrieving data from cloud storage and internal servers, and extracting key information using a natural language processing engine. This enables the automatic generation of business handover documents in a standardized template format. These generated documents are then visually displayed to aid understanding by the new business 담당자 (person in charge).

[0273] Furthermore, the server uses dialogue generation technology to analyze past dialogue records. This allows it to provide frequently asked questions and their answers in real time. This information is transmitted to user-operated information devices, such as smart glasses worn by factory workers. The smart glasses present the information visually and also serve as audio guides. This allows workers to efficiently access information without taking their hands off the device.

[0274] This system also provides interactive training. Through a terminal, users receive training that simulates work scenarios, allowing them to learn actual work procedures. For example, when a new staff member performs machine maintenance for the first time, a list of necessary tools and detailed instructions for each step are displayed on smart glasses, and voice guidance is played in the user's ear.

[0275] A concrete example of a prompt is as follows: "Generative AI model, summarize the routine maintenance procedure for the factory's shredder machine, along with the key points, and present it in a visually understandable format." By using prompts like this, even complex business procedures can be presented in an easy-to-understand manner.

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] The server automatically collects business materials from information media. The input is the connection information of cloud storage or in-house servers, and the output is a dataset of business materials. The server accesses these data sources through APIs, selects relevant materials using metadata and keywords, and stores them in a database.

[0279] Step 2:

[0280] The server extracts important information from the collected materials and generates a business handover document. The input is the dataset of business materials collected in Step 1, and the output is a business handover document in a standardized format. The server uses a natural language processing engine to analyze the text and formats the document according to a predefined template.

[0281] Step 3:

[0282] The server analyzes past conversation records using dialogue generation technology and generates common questions and their answers. The input is past conversation record data, and the output is a FAQ list. The server uses a generative AI model to recognize patterns in past records, extract common questions, and generate corresponding answers in natural language.

[0283] Step 4:

[0284] The server sends the generated business handover document and FAQs to an information device. The input is the list of business handover documents and FAQs, and the output is display data provided to the information device. The server transmits these data to the terminal via a network so that users can easily obtain information visually and auditorily.

[0285] Step 5:

[0286] The terminal provides interactive training to the user. The input is the display data and audio data received from the server, and the output is the feedback on the user's understanding and skill improvement. The terminal displays the work procedures and answers through the display and speaker of the smart glasses, tracks the user's operations, and records the progress.

[0287] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0288] The present invention realizes information provision and training considering the user's emotional state by incorporating an emotion engine into the business succession support system. This system has a mechanism for effectively exchanging information among the server, the terminal, and the user.

[0289] First, the server automatically collects business materials from the information medium, extracts important information using natural language processing technology as necessary, and generates a business succession document. At this time, the emotion engine considers the user's past response data and appropriately adjusts the tone and content of the information to be provided. For example, when stress is recognized, the document can be formatted in a concise and easy-to-understand form.

[0290] Furthermore, the server utilizes a conversation generation model to generate individual responses in real time based on inquiries from the user. The emotion engine analyzes the tone and text of the user's voice during the conversation and selects a dialogue style suitable for the user. As a specific example, when the user shows anxiety, more supportive answers and additional information provision can be made.

[0291] Users can participate in interactive training delivered via their devices, with the training content individually tailored by an emotion engine. This tailoring includes learning pace and feedback methods. To enhance user satisfaction, the system also includes a feature that provides timely motivational content based on progress.

[0292] Thus, by utilizing an emotion engine, this invention enables flexible job handover and training based on the user's emotional state, supporting an efficient and smooth transition of operations. This makes it possible to improve the user experience and operational efficiency.

[0293] The following describes the processing flow.

[0294] Step 1:

[0295] The server periodically scans information media and automatically collects business-related documents. This collection process uses metadata and keywords to select the most relevant files.

[0296] Step 2:

[0297] The server uses collected data to apply natural language processing techniques and extract important information. During this process, an emotion engine analyzes past user response data to adjust the tone and content of the document.

[0298] Step 3:

[0299] The server-generated handover documents are formatted and prepared for provision to the user. The formatted documents are summarized as needed and presented in a user-friendly format.

[0300] Step 4:

[0301] The conversation generation model is launched by the server, which generates common questions and their answers from past conversation records and current user input data. In this step, an emotion engine is used to judge the user's emotional state and optimize the conversation style.

[0302] Step 5:

[0303] The terminal displays the generated FAQs and interactive training modules to the user. The emotion engine analyzes the user's reaction in real time and dynamically adjusts the training content as needed.

[0304] Step 6:

[0305] The user receives training through the terminal and receives appropriate feedback provided by the emotion engine. This feedback includes motivation content considering the user's learning pace and emotional state.

[0306] (Example 2)

[0307] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In the handover of work, the collection of materials, the extraction of important information, and individual responses considering emotions are required, but there is a problem that it takes a great deal of labor and time to do this manually. In addition, there is a lack of a system for providing interactive training adapted to the individual learning pace and emotional state of the user to maintain and improve motivation.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0310] In this invention, the server includes means for automatically collecting documents from information media, means for automatically generating handover documents by analyzing important data, and means for analyzing emotional states and adjusting the tone and content of the data. This improves the efficiency of handover and enables the provision of individually optimized information and training to users.

[0311] An "information medium" is a device or system for storing and managing information in digital or physical form.

[0312] "Automated generation" refers to a process in which machines or software process data to create new documents or responses with minimal human intervention.

[0313] A "generative model" is an algorithm or artificial intelligence framework that learns from existing data and creates new information or responses.

[0314] "Interactive learning" is an educational process that transmits information through dialogue with the user and supports learning in a two-way manner.

[0315] "Emotional state" refers to the psychological or emotional state a user experiences, and is often a factor that influences their behavior and reactions.

[0316] "Motivation-enhancing content" refers to information and activities provided to stimulate users' motivation and interest.

[0317] This invention provides a system that streamlines job handover and training, and at its core is a structure in which a server and terminals work in coordination with each other.

[0318] The server automatically collects business documents from information media and extracts important information using natural language processing technology. The software used is a specialized text analysis engine designed to process large amounts of text data quickly.

[0319] Furthermore, the server is equipped with a generative AI model that analyzes the user's past conversation history to automatically generate frequently asked questions and their answers. In this process, an emotion engine analyzes the user's tone of voice and written text, adjusting the tone and content of the response to suit the user. For example, a user who is anxious about operating a new system will be given an explanation in a supportive tone. For instance, a prompt such as, "Please give advice to a novice user who is feeling stressed about the setup procedure for the new software," might be used.

[0320] The device provides users with personalized, interactive training. This involves displaying pre-configured learning materials sent from a server on the user's device, progressing at the user's pace. This interactive training incorporates assessments and feedback provided by an emotional engine to provide more appropriate guidance.

[0321] Furthermore, the device monitors the user's learning progress and provides motivational content based on that progress. For example, it displays a message of praise to users who complete a specific section and suggests new challenges to encourage further skill acquisition.

[0322] These features enable efficient handover of tasks and adaptive learning by users.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The server collects business data from information media. These information media include document management systems and mail servers. It searches for and collects relevant files and messages from these media. It obtains identifiers for business-related information as input and the collected raw data as output.

[0326] Step 2:

[0327] The server analyzes the collected data using natural language processing technology to extract important information. Specifically, a text analysis engine analyzes keywords and grammatical structures to identify key points of the business. It analyzes raw data as input and generates extracted important data as output.

[0328] Step 3:

[0329] The server uses an emotion engine to analyze the user's past emotional data and adjust the tone and content of important data. For example, if the user has shown stress in the past, the data will be simplified. The server uses the user's emotional data as input and generates an adjusted handover document as output.

[0330] Step 4:

[0331] The server utilizes a generative AI model to generate responses to user inquiries. It analyzes past dialogue history and creates the optimal response using the generative model. It uses real-time user input and dialogue history as input and provides individual responses as output.

[0332] Step 5:

[0333] The device provides users with interactive training. It displays learning materials according to the user's learning pace and supports their progress. It takes training content sent from the server as input and outputs training at a pace tailored to the user.

[0334] Step 6:

[0335] The server and terminal evaluate the user's training progress and provide motivational content. Once progress is confirmed, they present congratulatory messages and new challenges. As input, they analyze user progress data, and as output, they generate appropriate content.

[0336] (Application Example 2)

[0337] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0338] Traditional job handover and training systems do not provide flexible information that takes into account the emotional state of individual users. Therefore, stress and anxiety are not mitigated during the process of new employees or operators smoothly understanding and learning work procedures, potentially impacting work efficiency and productivity. Consequently, there is a need to personalize information provision and training based on users' emotions to ensure a smooth transition of work.

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

[0340] In this invention, the server includes a function to automatically collect information from a device that stores information related to business operations; a function to analyze important elements from the collected information and automatically generate business transition documents; a function to analyze past interaction records using a conversation generation algorithm and automatically generate typical questions and their answers; and a function to analyze the user's emotional state and adjust the content and tone of the instructions provided. This enables flexible and effective business handover and training tailored to the individual user's emotions.

[0341] "Information related to business operations" refers to data and knowledge necessary for business operations and the implementation of procedures.

[0342] "Device" refers to a component of hardware or software used to store information.

[0343] An "automatic data collection function" is a function that has a process for acquiring information without manual intervention, based on pre-programmed procedures.

[0344] A "function for analyzing important elements" refers to a function that has the process of identifying and extracting business-useful and essential information from a large amount of data.

[0345] A "business process transition document" is a document that organizes and describes the necessary procedures and knowledge to facilitate the transfer of information to new business personnel.

[0346] A "conversation generation algorithm" is a computational method that uses natural language processing technology to mimic human dialogue and construct rational responses.

[0347] "Interaction records" refer to the history of past conversations and communications, and are used as data for improvement and learning.

[0348] "Typical questions and answers" are intended to provide efficient information by listing frequently asked questions and their appropriate corresponding answers.

[0349] The "function that analyzes the user's emotional state and adjusts the content and tone of the instructions provided" is a function that judges the user's psychological state and dynamically optimizes the method of providing appropriate information accordingly.

[0350] This invention is a system that enables smooth job handover and training. The system automatically collects necessary data from a device that stores job-related information, analyzes important information based on this data, and creates job transition documents. Furthermore, the system uses a conversation generation algorithm to analyze past interaction records and dynamically generate typical questions and their answers. It also analyzes the user's emotional state and appropriately adjusts the content and tone of instructions to provide optimal information.

[0351] The server uses natural language processing libraries and sentiment analysis engines to analyze the tone of the user's voice and text in real time. Based on this analysis, it provides information that takes the user's mental state into account, supporting the learning process. Specifically, sentiment analysis tools such as IBM Watson and AWS Comprehend are used. Furthermore, OpenAI GPT-3 is used as the generative AI model to provide sophisticated conversational responses.

[0352] Users interact with this system via their terminals and receive information about their work in an easily understandable format. For example, when a new factory operator is undergoing training, the system not only explains the operating procedures in detail but also provides encouragement and supplementary information when it senses the operator's anxiety. This allows operators to work with greater confidence.

[0353] An example of a prompt sentence to be input into the generating AI model is, "Please provide a gentle way to instruct an operator who is feeling stressed on the production line." In this way, the present invention leverages its technological advancements to contribute to increased operational efficiency and improved user learning processes.

[0354] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0355] Step 1:

[0356] The server automatically collects data from devices that store information related to business operations. The input at this stage is raw data stored in the information storage device, and the output is organized business-related data. The server periodically monitors the devices and automatically retrieves new data when available.

[0357] Step 2:

[0358] The server analyzes key elements from the collected data and automatically generates business migration documents. The organized business-related data obtained in Step 1 is used as input. Natural language processing libraries (e.g., SpaCy or NLTK) are used to identify important information, and the output is a business migration document in a format that is easy for new business personnel to understand.

[0359] Step 3:

[0360] The server analyzes past interaction records using a conversation generation algorithm. The input is the collected history of past conversations. Based on this history, it generates typical questions and corresponding answers, automatically creating a list of frequently asked questions as output. By using a generation AI model (e.g., GPT-3), the high quality of the generated responses is guaranteed.

[0361] Step 4:

[0362] The server analyzes the user's emotional state and adjusts the content and tone of the information provided in real time. Input is user voice and text data, and the system uses an emotion analysis engine (e.g., IBM Watson, AWS Comprehend) to evaluate the user's emotions. The output generates flexible instructions tailored to the user's emotional state. For example, if the user is stressed, the system provides simpler and easier-to-understand instructions.

[0363] Step 5:

[0364] Users access business migration documents and FAQs generated through their terminals and receive interactive training. Input is learning materials provided by the server, and output is the user's learning progress and feedback. Motivational content and supplementary information are also provided to address any anxieties or questions users may have during their learning process.

[0365] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0366] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0371] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0372] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0373] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0374] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0375] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0376] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0377] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0378] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0379] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0380] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0381] The business handover support system according to the present invention forms a platform for effectively collecting and generating information between servers, terminals, and users. This system primarily satisfies the following requirements.

[0382] First, the server automatically collects business documents from cloud storage and internal servers as information sources. During this process, it analyzes metadata and keywords within documents to select business-related materials. For example, it can retrieve the latest project reports through the API of a project management tool.

[0383] Next, the server extracts important information from the collected data and generates a handover document using natural language processing technology. This process maintains information consistency by using standardized templates. Furthermore, the generated document clearly explains the background and procedures of the work and is provided to the user.

[0384] Furthermore, the server utilizes a conversation generation model to automatically generate frequently asked questions and their answers from past conversation records. This allows users to quickly resolve questions in their daily work. For example, in customer support operations, a list of FAQs based on past inquiries is published in real time, allowing new support staff to refer to it immediately.

[0385] Furthermore, this system includes means of providing interactive training. Users can learn interactively through a terminal, and the training modules are designed to align with work procedures. This allows new employees to improve their skills through training that simulates real-world work scenarios.

[0386] Thus, the present invention provides a system that reduces the time and effort required for business handover and supports efficient business transitions. Depending on the embodiment, the system can be flexibly customized and adapted to a variety of business scenarios.

[0387] The following describes the processing flow.

[0388] Step 1:

[0389] The server periodically scans the information media where business documents are stored. During this scan, it filters business-related files based on specific metadata and keywords, and retrieves the data via download or streaming.

[0390] Step 2:

[0391] Based on the data acquired by the server, natural language processing technology is used to analyze important sections and sentences. This analysis extracts the information necessary for handing over duties and automatically generates documents according to a template.

[0392] Step 3:

[0393] The server formats the generated handover documents and performs summarization as needed. As a result, the documents are optimized into a format that is easy for users to understand.

[0394] Step 4:

[0395] The server analyzes past email and chat history and uses a conversation generation model to automatically generate frequently asked questions and their answers. This process identifies key inquiry patterns based on frequency analysis.

[0396] Step 5:

[0397] The terminal displays generated FAQs and conversation modules on its interface, providing users with a state where inquiries can be handled in real time.

[0398] Step 6:

[0399] Users initiate interactive training through their devices and learn work procedures using training modules generated by the server. During this process, the user's progress is also recorded on the server via their devices.

[0400] (Example 1)

[0401] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0402] The challenges lie in streamlining information sharing during business handover and ensuring that new employees quickly acquire the necessary skills. In particular, the lack of a system for organizing necessary information from a large volume of documents and resolving questions by utilizing past conversation records may slow down the transition process.

[0403] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0404] In this invention, the server includes means for automatically collecting business information from an information storage device, means for analyzing important information from the collected information and automatically generating business transition documents, and means for analyzing previous dialogue history using a conversation generation algorithm and automatically generating frequently asked questions and their answers. This makes it possible for new business personnel to quickly and efficiently obtain the necessary information and understand the business, significantly reducing the time and effort required during business transition.

[0405] An "information storage device" is a device for storing and managing digital data and information, and includes cloud storage and internal servers.

[0406] "Business information" refers to documents, data, reports, and other related materials that are associated with business activities and enable the performance of those activities.

[0407] "Methods for automatic collection" refers to a system that uses specific algorithms or programs to acquire necessary business information from information storage devices without human intervention.

[0408] "Methods for analyzing and automatically generating business transition documents" refers to methods that use natural language processing technology and algorithms to analyze collected information and automatically create documents based on that information.

[0409] A "conversation generation algorithm" is a computational procedure or process that analyzes past dialogues and document data to generate frequently asked questions and appropriate responses.

[0410] "Previous dialogue history" refers to records and logs of past communications, and this data is used to leverage past insights by analyzing it.

[0411] "Methods of automatic generation" refer to methods of processing data under specific conditions based on a program to produce results.

[0412] The business handover support system according to the present invention has a structure in which a server, terminal, and user work together.

[0413] The server accesses information storage devices and automatically collects business information. Using cloud storage or internal servers, it retrieves information through specific algorithms and stores it in digital format. The server can then analyze the information using natural language processing techniques. Specifically, it processes text using libraries such as "Python's NLTK" and "spaCy" to extract important information necessary for business migration.

[0414] Based on the analyzed information, the server generates business migration documents. This is done using standardized templates, automatically creating consistent documents that reduce human error and improve operational efficiency.

[0415] Furthermore, the server uses a conversation generation algorithm to analyze previous conversation history and generate FAQs (Frequently Asked Questions). This allows users to quickly obtain answers to frequently asked questions. The server utilizes a "generative AI model" to build FAQs that include responses in natural language.

[0416] Users can use their own devices to access the provided business migration documents and FAQs to deepen their understanding of their work. Through this system, users can check the workflow and quickly obtain the information they need for their work.

[0417] As a concrete example, here are some examples of prompt statements for a generative AI model:

[0418] "Please extract the key points from the latest report on the ongoing project and create a clear and concise summary for the new person in charge."

[0419] Such prompts allow the server to quickly process the necessary information and present it to the user. This enables the user to review documents without wasting time and to quickly take over tasks or start new work.

[0420] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0421] Step 1:

[0422] The server collects business information from information storage devices. Inputs include documents stored in cloud storage or on internal servers. The server automatically accesses these documents using an API and downloads the necessary files. The output is a set of collected digital documents.

[0423] Step 2:

[0424] The server analyzes the collected documents using natural language processing techniques. The input is the documents collected in step 1. The server uses libraries such as "Python's NLTK" and "spaCy" to perform text mining on these documents and extract important information and keywords. The output is a list of the extracted important information.

[0425] Step 3:

[0426] The server generates business migration documents based on the extracted information. The input is a list of important information obtained in Step 2. The server uses a standardized template to automatically generate documents while maintaining information consistency. The output is a consistent business migration document.

[0427] Step 4:

[0428] The server generates FAQs using a conversation generation algorithm. The input is past conversation history data. The server analyzes the past conversation data and automatically generates frequently asked questions and their answers. The output is an FAQ list.

[0429] Step 5:

[0430] Users review the generated business migration documents and FAQs via their terminals and utilize them in their work. The input consists of business migration documents and FAQs provided by the server. Users access the materials via their terminals, understand the necessary information, and proceed with their work. The output is improved user understanding and work efficiency.

[0431] (Application Example 1)

[0432] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] In modern industry, the handover of tasks is a time-consuming and labor-intensive challenge. Especially in manufacturing, where diverse procedures and rules exist, it is difficult for new workers to adapt immediately. Furthermore, failure to efficiently transfer the knowledge of experienced workers can lead to a decline in quality and production efficiency. Therefore, there is a need for new systems that enable effective task handover and support the skill development of workers.

[0434] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0435] In this invention, the server includes means for automatically collecting business data from an information medium storing such data, means for extracting important information from the collected data and automatically generating business handover documents, means for analyzing past dialogue records using dialogue generation technology and automatically generating frequently asked questions and their answers, and display and audio guide devices for visually and audibly presenting business procedures and answers to questions. As a result, new employees can proceed with their work while receiving accurate information in real time, thereby improving overall business efficiency.

[0436] "Business documents" refer to a collection of documents and data used by companies and organizations for their daily operations and business execution.

[0437] An "information medium" is a physical or digital platform for storing or transferring data or documents.

[0438] "Means" refers to the methods or technical measures used to achieve a specific objective.

[0439] The term "device" refers to a machine or system designed to perform a specific function.

[0440] "Dialogue generation technology" is a technology that automatically generates dialogue in text or voice using natural language processing.

[0441] "Interactive training" is a learning format in which users actively participate and progress while receiving feedback.

[0442] "Information equipment" refers to electronic devices and digital tools used for data input, processing, and output.

[0443] "Presenting visually and aurally" refers to communicating information to users using displays and sound.

[0444] This invention is a system for facilitating the smooth handover of business operations, in which a server, terminal, and user work together to effectively realize its functions. Specific embodiments are described below.

[0445] The server first automatically collects business documents from various information sources. This process involves retrieving data from cloud storage and internal servers, and extracting key information using a natural language processing engine. This enables the automatic generation of business handover documents in a standardized template format. These generated documents are then visually displayed to aid understanding by the new business 담당자 (person in charge).

[0446] Furthermore, the server uses dialogue generation technology to analyze past dialogue records. This allows it to provide frequently asked questions and their answers in real time. This information is transmitted to user-operated information devices, such as smart glasses worn by factory workers. The smart glasses present the information visually and also serve as audio guides. This allows workers to efficiently access information without taking their hands off the device.

[0447] This system also provides interactive training. Through a terminal, users receive training that simulates work scenarios, allowing them to learn actual work procedures. For example, when a new staff member performs machine maintenance for the first time, a list of necessary tools and detailed instructions for each step are displayed on smart glasses, and voice guidance is played in the user's ear.

[0448] A concrete example of a prompt is as follows: "Generative AI model, summarize the routine maintenance procedure for the factory's shredder machine, along with the key points, and present it in a visually understandable format." By using prompts like this, even complex business procedures can be presented in an easy-to-understand manner.

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The server automatically collects business documents from various information sources. Inputs include connection information for cloud storage and internal servers, while output is a dataset of business documents. The server accesses these data sources via APIs, uses metadata and keywords to select relevant documents, and stores them in a database.

[0452] Step 2:

[0453] The server extracts important information from the collected materials and generates a business handover document. The input is a dataset of business materials collected in step 1, and the output is a business handover document in a standardized format. The server uses a natural language processing engine to parse the text and format the document according to a predetermined template.

[0454] Step 3:

[0455] The server uses dialogue generation technology to analyze past dialogue records and generate frequently asked questions and their answers. The input is past dialogue record data, and the output is an FAQ list. The server uses a generation AI model to recognize patterns in past records, extract frequently asked questions, and generate corresponding answers in natural language.

[0456] Step 4:

[0457] The server transmits the generated handover documents and FAQs to the information device. The input is a list of handover documents and FAQs, and the output is display data provided to the information device. The server transmits this data to the terminal via the network, making it easy for users to obtain the information visually and aurally.

[0458] Step 5:

[0459] The device provides users with interactive training. Input consists of display and audio data received from the server, while output is feedback on the user's understanding and skill improvement. The device displays work procedures and answers through the smart glasses' display and speaker, and tracks user actions to record progress.

[0460] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0461] This invention incorporates an emotion engine into a business handover support system to provide information and training that takes into account the user's emotional state. This system includes a mechanism for effectively exchanging information between the server, terminal, and user.

[0462] The server first automatically collects business documents from information media, extracts important information using natural language processing technology as needed, and generates business handover documents. During this process, an emotion engine considers the user's past response data and appropriately adjusts the tone and content of the information provided. For example, if stress is detected, the document can be restructured to be concise and easy to understand.

[0463] Furthermore, the server utilizes a conversation generation model to generate personalized responses in real time based on user inquiries. The emotion engine analyzes the user's tone of voice and text during the conversation and selects a dialogue style appropriate for the user. For example, if the user is showing signs of anxiety, it can provide more supportive answers or additional information.

[0464] Users can participate in interactive training delivered via their devices, with the training content individually tailored by an emotion engine. This tailoring includes learning pace and feedback methods. To enhance user satisfaction, the system also includes a feature that provides timely motivational content based on progress.

[0465] Thus, by utilizing an emotion engine, this invention enables flexible job handover and training based on the user's emotional state, supporting an efficient and smooth transition of operations. This makes it possible to improve the user experience and operational efficiency.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] The server periodically scans information media and automatically collects business-related documents. This collection process uses metadata and keywords to select the most relevant files.

[0469] Step 2:

[0470] The server uses collected data to apply natural language processing techniques and extract important information. During this process, an emotion engine analyzes past user response data to adjust the tone and content of the document.

[0471] Step 3:

[0472] The server-generated handover documents are formatted and prepared for provision to the user. The formatted documents are summarized as needed and presented in a user-friendly format.

[0473] Step 4:

[0474] The conversation generation model is activated by the server and generates frequently asked questions and their answers from past dialogue records and current user input data. In this step, the emotion engine is used to determine the user's emotional state and optimize the dialogue style.

[0475] Step 5:

[0476] The device displays generated FAQs and interactive training modules to the user. The emotion engine analyzes the user's responses in real time and dynamically adjusts the training content as needed.

[0477] Step 6:

[0478] Users receive training through their devices and appropriate feedback provided by an emotion engine. This feedback includes motivational content that takes into account the user's learning pace and emotional state.

[0479] (Example 2)

[0480] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] During the handover of work responsibilities, tasks such as collecting documents, extracting important information, and providing individualized support that takes emotions into consideration are required, but performing these tasks manually is extremely time-consuming and laborious. Furthermore, there is a lack of systems that provide interactive training adapted to each user's learning pace and emotional state, and that maintain and improve motivation.

[0482] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0483] In this invention, the server includes means for automatically collecting documents from information media, means for automatically generating handover documents by analyzing important data, and means for analyzing emotional states and adjusting the tone and content of the data. This improves the efficiency of handover and enables the provision of individually optimized information and training to users.

[0484] An "information medium" is a device or system for storing and managing information in digital or physical form.

[0485] "Automated generation" refers to a process in which machines or software process data to create new documents or responses with minimal human intervention.

[0486] A "generative model" is an algorithm or artificial intelligence framework that learns from existing data and creates new information or responses.

[0487] "Interactive learning" is an educational process that transmits information through dialogue with the user and supports learning in a two-way manner.

[0488] "Emotional state" refers to the psychological or emotional state a user experiences, and is often a factor that influences their behavior and reactions.

[0489] "Motivation-enhancing content" refers to information and activities provided to stimulate users' motivation and interest.

[0490] This invention provides a system that streamlines job handover and training, and at its core is a structure in which a server and terminals work in coordination with each other.

[0491] The server automatically collects business documents from information media and extracts important information using natural language processing technology. The software used is a specialized text analysis engine designed to process large amounts of text data quickly.

[0492] Furthermore, the server is equipped with a generative AI model that analyzes the user's past conversation history to automatically generate frequently asked questions and their answers. In this process, an emotion engine analyzes the user's tone of voice and written text, adjusting the tone and content of the response to suit the user. For example, a user who is anxious about operating a new system will be given an explanation in a supportive tone. For instance, a prompt such as, "Please give advice to a novice user who is feeling stressed about the setup procedure for the new software," might be used.

[0493] The device provides users with personalized, interactive training. This involves displaying pre-configured learning materials sent from a server on the user's device, progressing at the user's pace. This interactive training incorporates assessments and feedback provided by an emotional engine to provide more appropriate guidance.

[0494] Furthermore, the device monitors the user's learning progress and provides motivational content based on that progress. For example, it displays a message of praise to users who complete a specific section and suggests new challenges to encourage further skill acquisition.

[0495] These features enable efficient handover of tasks and adaptive learning by users.

[0496] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0497] Step 1:

[0498] The server collects business data from information media. These information media include document management systems and mail servers. It searches for and collects relevant files and messages from these media. It obtains identifiers for business-related information as input and the collected raw data as output.

[0499] Step 2:

[0500] The server analyzes the collected data using natural language processing technology to extract important information. Specifically, a text analysis engine analyzes keywords and grammatical structures to identify key points of the business. It analyzes raw data as input and generates extracted important data as output.

[0501] Step 3:

[0502] The server uses an emotion engine to analyze the user's past emotional data and adjust the tone and content of important data. For example, if the user has shown stress in the past, the data will be simplified. The server uses the user's emotional data as input and generates an adjusted handover document as output.

[0503] Step 4:

[0504] The server utilizes a generative AI model to generate responses to user inquiries. It analyzes past dialogue history and creates the optimal response using the generative model. It uses real-time user input and dialogue history as input and provides individual responses as output.

[0505] Step 5:

[0506] The device provides users with interactive training. It displays learning materials according to the user's learning pace and supports their progress. It takes training content sent from the server as input and outputs training at a pace tailored to the user.

[0507] Step 6:

[0508] The server and terminal evaluate the user's training progress and provide motivational content. Once progress is confirmed, they present congratulatory messages and new challenges. As input, they analyze user progress data, and as output, they generate appropriate content.

[0509] (Application Example 2)

[0510] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Traditional job handover and training systems do not provide flexible information that takes into account the emotional state of individual users. Therefore, stress and anxiety are not mitigated during the process of new employees or operators smoothly understanding and learning work procedures, potentially impacting work efficiency and productivity. Consequently, there is a need to personalize information provision and training based on users' emotions to ensure a smooth transition of work.

[0512] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0513] In this invention, the server includes a function to automatically collect information from a device that stores information related to business operations; a function to analyze important elements from the collected information and automatically generate business transition documents; a function to analyze past interaction records using a conversation generation algorithm and automatically generate typical questions and their answers; and a function to analyze the user's emotional state and adjust the content and tone of the instructions provided. This enables flexible and effective business handover and training tailored to the individual user's emotions.

[0514] "Information related to business operations" refers to data and knowledge necessary for business operations and the implementation of procedures.

[0515] "Device" refers to a component of hardware or software used to store information.

[0516] An "automatic data collection function" is a function that has a process for acquiring information without manual intervention, based on pre-programmed procedures.

[0517] A "function for analyzing important elements" refers to a function that has the process of identifying and extracting business-useful and essential information from a large amount of data.

[0518] A "business process transition document" is a document that organizes and describes the necessary procedures and knowledge to facilitate the transfer of information to new business personnel.

[0519] A "conversation generation algorithm" is a computational method that uses natural language processing technology to mimic human dialogue and construct rational responses.

[0520] "Interaction records" refer to the history of past conversations and communications, and are used as data for improvement and learning.

[0521] "Typical questions and answers" are intended to provide efficient information by listing frequently asked questions and their appropriate corresponding answers.

[0522] The "function that analyzes the user's emotional state and adjusts the content and tone of the instructions provided" is a function that judges the user's psychological state and dynamically optimizes the method of providing appropriate information accordingly.

[0523] This invention is a system that enables smooth job handover and training. The system automatically collects necessary data from a device that stores job-related information, analyzes important information based on this data, and creates job transition documents. Furthermore, the system uses a conversation generation algorithm to analyze past interaction records and dynamically generate typical questions and their answers. It also analyzes the user's emotional state and appropriately adjusts the content and tone of instructions to provide optimal information.

[0524] The server uses natural language processing libraries and sentiment analysis engines to analyze the tone of the user's voice and text in real time. Based on this analysis, it provides information that takes the user's mental state into account, supporting the learning process. Specifically, sentiment analysis tools such as IBM Watson and AWS Comprehend are used. Furthermore, OpenAI GPT-3 is used as the generative AI model to provide sophisticated conversational responses.

[0525] Users interact with this system via their terminals and receive information about their work in an easily understandable format. For example, when a new factory operator is undergoing training, the system not only explains the operating procedures in detail but also provides encouragement and supplementary information when it senses the operator's anxiety. This allows operators to work with greater confidence.

[0526] An example of a prompt sentence to be input into the generating AI model is, "Please provide a gentle way to instruct an operator who is feeling stressed on the production line." In this way, the present invention leverages its technological advancements to contribute to increased operational efficiency and improved user learning processes.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The server automatically collects data from devices that store information related to business operations. The input at this stage is raw data stored in the information storage device, and the output is organized business-related data. The server periodically monitors the devices and automatically retrieves new data when available.

[0530] Step 2:

[0531] The server analyzes key elements from the collected data and automatically generates business migration documents. The organized business-related data obtained in Step 1 is used as input. Natural language processing libraries (e.g., SpaCy or NLTK) are used to identify important information, and the output is a business migration document in a format that is easy for new business personnel to understand.

[0532] Step 3:

[0533] The server analyzes past interaction records using a conversation generation algorithm. The input is the collected history of past conversations. Based on this history, it generates typical questions and corresponding answers, automatically creating a list of frequently asked questions as output. By using a generation AI model (e.g., GPT-3), the high quality of the generated responses is guaranteed.

[0534] Step 4:

[0535] The server analyzes the user's emotional state and adjusts the content and tone of the information provided in real time. Input is user voice and text data, and the system uses an emotion analysis engine (e.g., IBM Watson, AWS Comprehend) to evaluate the user's emotions. The output generates flexible instructions tailored to the user's emotional state. For example, if the user is stressed, the system provides simpler and easier-to-understand instructions.

[0536] Step 5:

[0537] Users access business migration documents and FAQs generated through their terminals and receive interactive training. Input is learning materials provided by the server, and output is the user's learning progress and feedback. Motivational content and supplementary information are also provided to address any anxieties or questions users may have during their learning process.

[0538] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0539] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0540] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0541] [Fourth Embodiment]

[0542] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0543] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0544] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0545] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0546] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0547] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0548] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0549] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0550] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0551] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0552] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0553] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0554] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0555] The business handover support system according to the present invention forms a platform for effectively collecting and generating information between servers, terminals, and users. This system primarily satisfies the following requirements.

[0556] First, the server automatically collects business documents from cloud storage and internal servers as information sources. During this process, it analyzes metadata and keywords within documents to select business-related materials. For example, it can retrieve the latest project reports through the API of a project management tool.

[0557] Next, the server extracts important information from the collected data and generates a handover document using natural language processing technology. This process maintains information consistency by using standardized templates. Furthermore, the generated document clearly explains the background and procedures of the work and is provided to the user.

[0558] Furthermore, the server utilizes a conversation generation model to automatically generate frequently asked questions and their answers from past conversation records. This allows users to quickly resolve questions in their daily work. For example, in customer support operations, a list of FAQs based on past inquiries is published in real time, allowing new support staff to refer to it immediately.

[0559] Furthermore, this system includes means of providing interactive training. Users can learn interactively through a terminal, and the training modules are designed to align with work procedures. This allows new employees to improve their skills through training that simulates real-world work scenarios.

[0560] Thus, the present invention provides a system that reduces the time and effort required for business handover and supports efficient business transitions. Depending on the embodiment, the system can be flexibly customized and adapted to a variety of business scenarios.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] The server periodically scans the information media where business documents are stored. During this scan, it filters business-related files based on specific metadata and keywords, and retrieves the data via download or streaming.

[0564] Step 2:

[0565] Based on the data acquired by the server, natural language processing technology is used to analyze important sections and sentences. This analysis extracts the information necessary for handing over duties and automatically generates documents according to a template.

[0566] Step 3:

[0567] The server formats the generated handover documents and performs summarization as needed. As a result, the documents are optimized into a format that is easy for users to understand.

[0568] Step 4:

[0569] The server analyzes past email and chat history and uses a conversation generation model to automatically generate frequently asked questions and their answers. This process identifies key inquiry patterns based on frequency analysis.

[0570] Step 5:

[0571] The terminal displays generated FAQs and conversation modules on its interface, providing users with a state where inquiries can be handled in real time.

[0572] Step 6:

[0573] Users initiate interactive training through their devices and learn work procedures using training modules generated by the server. During this process, the user's progress is also recorded on the server via their devices.

[0574] (Example 1)

[0575] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] The challenges lie in streamlining information sharing during business handover and ensuring that new employees quickly acquire the necessary skills. In particular, the lack of a system for organizing necessary information from a large volume of documents and resolving questions by utilizing past conversation records may slow down the transition process.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0578] In this invention, the server includes means for automatically collecting business information from an information storage device, means for analyzing important information from the collected information and automatically generating business transition documents, and means for analyzing previous dialogue history using a conversation generation algorithm and automatically generating frequently asked questions and their answers. This makes it possible for new business personnel to quickly and efficiently obtain the necessary information and understand the business, significantly reducing the time and effort required during business transition.

[0579] An "information storage device" is a device for storing and managing digital data and information, and includes cloud storage and internal servers.

[0580] "Business information" refers to documents, data, reports, and other related materials that are associated with business activities and enable the performance of those activities.

[0581] "Methods for automatic collection" refers to a system that uses specific algorithms or programs to acquire necessary business information from information storage devices without human intervention.

[0582] "Methods for analyzing and automatically generating business transition documents" refers to methods that use natural language processing technology and algorithms to analyze collected information and automatically create documents based on that information.

[0583] A "conversation generation algorithm" is a computational procedure or process that analyzes past dialogues and document data to generate frequently asked questions and appropriate responses.

[0584] "Previous dialogue history" refers to records and logs of past communications, and this data is used to leverage past insights by analyzing it.

[0585] "Methods of automatic generation" refer to methods of processing data under specific conditions based on a program to produce results.

[0586] The business handover support system according to the present invention has a structure in which a server, terminal, and user work together.

[0587] The server accesses information storage devices and automatically collects business information. Using cloud storage or internal servers, it retrieves information through specific algorithms and stores it in digital format. The server can then analyze the information using natural language processing techniques. Specifically, it processes text using libraries such as "Python's NLTK" and "spaCy" to extract important information necessary for business migration.

[0588] Based on the analyzed information, the server generates business migration documents. This is done using standardized templates, automatically creating consistent documents that reduce human error and improve operational efficiency.

[0589] Furthermore, the server uses a conversation generation algorithm to analyze previous conversation history and generate FAQs (Frequently Asked Questions). This allows users to quickly obtain answers to frequently asked questions. The server utilizes a "generative AI model" to build FAQs that include responses in natural language.

[0590] Users can use their own devices to access the provided business migration documents and FAQs to deepen their understanding of their work. Through this system, users can check the workflow and quickly obtain the information they need for their work.

[0591] As a concrete example, here are some examples of prompt statements for a generative AI model:

[0592] "Please extract the key points from the latest report on the ongoing project and create a clear and concise summary for the new person in charge."

[0593] Such prompts allow the server to quickly process the necessary information and present it to the user. This enables the user to review documents without wasting time and to quickly take over tasks or start new work.

[0594] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0595] Step 1:

[0596] The server collects business information from information storage devices. Inputs include documents stored in cloud storage or on internal servers. The server automatically accesses these documents using an API and downloads the necessary files. The output is a set of collected digital documents.

[0597] Step 2:

[0598] The server analyzes the collected documents using natural language processing techniques. The input is the documents collected in step 1. The server uses libraries such as "Python's NLTK" and "spaCy" to perform text mining on these documents and extract important information and keywords. The output is a list of the extracted important information.

[0599] Step 3:

[0600] The server generates business migration documents based on the extracted information. The input is a list of important information obtained in Step 2. The server uses a standardized template to automatically generate documents while maintaining information consistency. The output is a consistent business migration document.

[0601] Step 4:

[0602] The server generates FAQs using a conversation generation algorithm. The input is past conversation history data. The server analyzes the past conversation data and automatically generates frequently asked questions and their answers. The output is an FAQ list.

[0603] Step 5:

[0604] Users review the generated business migration documents and FAQs via their terminals and utilize them in their work. The input consists of business migration documents and FAQs provided by the server. Users access the materials via their terminals, understand the necessary information, and proceed with their work. The output is improved user understanding and work efficiency.

[0605] (Application Example 1)

[0606] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] In modern industry, the handover of tasks is a time-consuming and labor-intensive challenge. Especially in manufacturing, where diverse procedures and rules exist, it is difficult for new workers to adapt immediately. Furthermore, failure to efficiently transfer the knowledge of experienced workers can lead to a decline in quality and production efficiency. Therefore, there is a need for new systems that enable effective task handover and support the skill development of workers.

[0608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0609] In this invention, the server includes means for automatically collecting business data from an information medium storing such data, means for extracting important information from the collected data and automatically generating business handover documents, means for analyzing past dialogue records using dialogue generation technology and automatically generating frequently asked questions and their answers, and display and audio guide devices for visually and audibly presenting business procedures and answers to questions. As a result, new employees can proceed with their work while receiving accurate information in real time, thereby improving overall business efficiency.

[0610] "Business documents" refer to a collection of documents and data used by companies and organizations for their daily operations and business execution.

[0611] An "information medium" is a physical or digital platform for storing or transferring data or documents.

[0612] "Means" refers to the methods or technical measures used to achieve a specific objective.

[0613] The term "device" refers to a machine or system designed to perform a specific function.

[0614] "Dialogue generation technology" is a technology that automatically generates dialogue in text or voice using natural language processing.

[0615] "Interactive training" is a learning format in which users actively participate and progress while receiving feedback.

[0616] "Information equipment" refers to electronic devices and digital tools used for data input, processing, and output.

[0617] "Presenting visually and aurally" refers to communicating information to users using displays and sound.

[0618] This invention is a system for facilitating the smooth handover of business operations, in which a server, terminal, and user work together to effectively realize its functions. Specific embodiments are described below.

[0619] The server first automatically collects business documents from various information sources. This process involves retrieving data from cloud storage and internal servers, and extracting key information using a natural language processing engine. This enables the automatic generation of business handover documents in a standardized template format. These generated documents are then visually displayed to aid understanding by the new business 담당자 (person in charge).

[0620] Furthermore, the server uses dialogue generation technology to analyze past dialogue records. This allows it to provide frequently asked questions and their answers in real time. This information is transmitted to user-operated information devices, such as smart glasses worn by factory workers. The smart glasses present the information visually and also serve as audio guides. This allows workers to efficiently access information without taking their hands off the device.

[0621] This system also provides interactive training. Through a terminal, users receive training that simulates work scenarios, allowing them to learn actual work procedures. For example, when a new staff member performs machine maintenance for the first time, a list of necessary tools and detailed instructions for each step are displayed on smart glasses, and voice guidance is played in the user's ear.

[0622] A concrete example of a prompt is as follows: "Generative AI model, summarize the routine maintenance procedure for the factory's shredder machine, along with the key points, and present it in a visually understandable format." By using prompts like this, even complex business procedures can be presented in an easy-to-understand manner.

[0623] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0624] Step 1:

[0625] The server automatically collects business documents from various information sources. Inputs include connection information for cloud storage and internal servers, while output is a dataset of business documents. The server accesses these data sources via APIs, uses metadata and keywords to select relevant documents, and stores them in a database.

[0626] Step 2:

[0627] The server extracts important information from the collected materials and generates a business handover document. The input is a dataset of business materials collected in step 1, and the output is a business handover document in a standardized format. The server uses a natural language processing engine to parse the text and format the document according to a predetermined template.

[0628] Step 3:

[0629] The server uses dialogue generation technology to analyze past dialogue records and generate frequently asked questions and their answers. The input is past dialogue record data, and the output is an FAQ list. The server uses a generation AI model to recognize patterns in past records, extract frequently asked questions, and generate corresponding answers in natural language.

[0630] Step 4:

[0631] The server transmits the generated handover documents and FAQs to the information device. The input is a list of handover documents and FAQs, and the output is display data provided to the information device. The server transmits this data to the terminal via the network, making it easy for users to obtain the information visually and aurally.

[0632] Step 5:

[0633] The device provides users with interactive training. Input consists of display and audio data received from the server, while output is feedback on the user's understanding and skill improvement. The device displays work procedures and answers through the smart glasses' display and speaker, and tracks user actions to record progress.

[0634] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0635] This invention incorporates an emotion engine into a business handover support system to provide information and training that takes into account the user's emotional state. This system includes a mechanism for effectively exchanging information between the server, terminal, and user.

[0636] The server first automatically collects business documents from information media, extracts important information using natural language processing technology as needed, and generates business handover documents. During this process, an emotion engine considers the user's past response data and appropriately adjusts the tone and content of the information provided. For example, if stress is detected, the document can be restructured to be concise and easy to understand.

[0637] Furthermore, the server utilizes a conversation generation model to generate personalized responses in real time based on user inquiries. The emotion engine analyzes the user's tone of voice and text during the conversation and selects a dialogue style appropriate for the user. For example, if the user is showing signs of anxiety, it can provide more supportive answers or additional information.

[0638] Users can participate in interactive training delivered via their devices, with the training content individually tailored by an emotion engine. This tailoring includes learning pace and feedback methods. To enhance user satisfaction, the system also includes a feature that provides timely motivational content based on progress.

[0639] Thus, by utilizing an emotion engine, this invention enables flexible job handover and training based on the user's emotional state, supporting an efficient and smooth transition of operations. This makes it possible to improve the user experience and operational efficiency.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The server periodically scans information media and automatically collects business-related documents. This collection process uses metadata and keywords to select the most relevant files.

[0643] Step 2:

[0644] The server uses collected data to apply natural language processing techniques and extract important information. During this process, an emotion engine analyzes past user response data to adjust the tone and content of the document.

[0645] Step 3:

[0646] The server-generated handover documents are formatted and prepared for provision to the user. The formatted documents are summarized as needed and presented in a user-friendly format.

[0647] Step 4:

[0648] The conversation generation model is activated by the server and generates frequently asked questions and their answers from past dialogue records and current user input data. In this step, the emotion engine is used to determine the user's emotional state and optimize the dialogue style.

[0649] Step 5:

[0650] The device displays generated FAQs and interactive training modules to the user. The emotion engine analyzes the user's responses in real time and dynamically adjusts the training content as needed.

[0651] Step 6:

[0652] Users receive training through their devices and appropriate feedback provided by an emotion engine. This feedback includes motivational content that takes into account the user's learning pace and emotional state.

[0653] (Example 2)

[0654] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] During the handover of work responsibilities, tasks such as collecting documents, extracting important information, and providing individualized support that takes emotions into consideration are required, but performing these tasks manually is extremely time-consuming and laborious. Furthermore, there is a lack of systems that provide interactive training adapted to each user's learning pace and emotional state, and that maintain and improve motivation.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0657] In this invention, the server includes means for automatically collecting documents from information media, means for automatically generating handover documents by analyzing important data, and means for analyzing emotional states and adjusting the tone and content of the data. This improves the efficiency of handover and enables the provision of individually optimized information and training to users.

[0658] An "information medium" is a device or system for storing and managing information in digital or physical form.

[0659] "Automated generation" refers to a process in which machines or software process data to create new documents or responses with minimal human intervention.

[0660] A "generative model" is an algorithm or artificial intelligence framework that learns from existing data and creates new information or responses.

[0661] "Interactive learning" is an educational process that transmits information through dialogue with the user and supports learning in a two-way manner.

[0662] "Emotional state" refers to the psychological or emotional state a user experiences, and is often a factor that influences their behavior and reactions.

[0663] "Motivation-enhancing content" refers to information and activities provided to stimulate users' motivation and interest.

[0664] This invention provides a system that streamlines job handover and training, and at its core is a structure in which a server and terminals work in coordination with each other.

[0665] The server automatically collects business documents from information media and extracts important information using natural language processing technology. The software used is a specialized text analysis engine designed to process large amounts of text data quickly.

[0666] Furthermore, the server is equipped with a generative AI model that analyzes the user's past conversation history to automatically generate frequently asked questions and their answers. In this process, an emotion engine analyzes the user's tone of voice and written text, adjusting the tone and content of the response to suit the user. For example, a user who is anxious about operating a new system will be given an explanation in a supportive tone. For instance, a prompt such as, "Please give advice to a novice user who is feeling stressed about the setup procedure for the new software," might be used.

[0667] The device provides users with personalized, interactive training. This involves displaying pre-configured learning materials sent from a server on the user's device, progressing at the user's pace. This interactive training incorporates assessments and feedback provided by an emotional engine to provide more appropriate guidance.

[0668] Furthermore, the device monitors the user's learning progress and provides motivational content based on that progress. For example, it displays a message of praise to users who complete a specific section and suggests new challenges to encourage further skill acquisition.

[0669] These features enable efficient handover of tasks and adaptive learning by users.

[0670] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0671] Step 1:

[0672] The server collects business data from information media. These information media include document management systems and mail servers. It searches for and collects relevant files and messages from these media. It obtains identifiers for business-related information as input and the collected raw data as output.

[0673] Step 2:

[0674] The server analyzes the collected data using natural language processing technology to extract important information. Specifically, a text analysis engine analyzes keywords and grammatical structures to identify key points of the business. It analyzes raw data as input and generates extracted important data as output.

[0675] Step 3:

[0676] The server uses an emotion engine to analyze the user's past emotional data and adjust the tone and content of important data. For example, if the user has shown stress in the past, the data will be simplified. The server uses the user's emotional data as input and generates an adjusted handover document as output.

[0677] Step 4:

[0678] The server utilizes a generative AI model to generate responses to user inquiries. It analyzes past dialogue history and creates the optimal response using the generative model. It uses real-time user input and dialogue history as input and provides individual responses as output.

[0679] Step 5:

[0680] The device provides users with interactive training. It displays learning materials according to the user's learning pace and supports their progress. It takes training content sent from the server as input and outputs training at a pace tailored to the user.

[0681] Step 6:

[0682] The server and terminal evaluate the user's training progress and provide motivational content. Once progress is confirmed, they present congratulatory messages and new challenges. As input, they analyze user progress data, and as output, they generate appropriate content.

[0683] (Application Example 2)

[0684] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Traditional job handover and training systems do not provide flexible information that takes into account the emotional state of individual users. Therefore, stress and anxiety are not mitigated during the process of new employees or operators smoothly understanding and learning work procedures, potentially impacting work efficiency and productivity. Consequently, there is a need to personalize information provision and training based on users' emotions to ensure a smooth transition of work.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0687] In this invention, the server includes a function to automatically collect information from a device that stores information related to business operations; a function to analyze important elements from the collected information and automatically generate business transition documents; a function to analyze past interaction records using a conversation generation algorithm and automatically generate typical questions and their answers; and a function to analyze the user's emotional state and adjust the content and tone of the instructions provided. This enables flexible and effective business handover and training tailored to the individual user's emotions.

[0688] "Information related to business operations" refers to data and knowledge necessary for business operations and the implementation of procedures.

[0689] "Device" refers to a component of hardware or software used to store information.

[0690] An "automatic data collection function" is a function that has a process for acquiring information without manual intervention, based on pre-programmed procedures.

[0691] A "function for analyzing important elements" refers to a function that has the process of identifying and extracting business-useful and essential information from a large amount of data.

[0692] A "business process transition document" is a document that organizes and describes the necessary procedures and knowledge to facilitate the transfer of information to new business personnel.

[0693] A "conversation generation algorithm" is a computational method that uses natural language processing technology to mimic human dialogue and construct rational responses.

[0694] "Interaction records" refer to the history of past conversations and communications, and are used as data for improvement and learning.

[0695] "Typical questions and answers" are intended to provide efficient information by listing frequently asked questions and their appropriate corresponding answers.

[0696] The "function that analyzes the user's emotional state and adjusts the content and tone of the instructions provided" is a function that judges the user's psychological state and dynamically optimizes the method of providing appropriate information accordingly.

[0697] This invention is a system that enables smooth job handover and training. The system automatically collects necessary data from a device that stores job-related information, analyzes important information based on this data, and creates job transition documents. Furthermore, the system uses a conversation generation algorithm to analyze past interaction records and dynamically generate typical questions and their answers. It also analyzes the user's emotional state and appropriately adjusts the content and tone of instructions to provide optimal information.

[0698] The server uses natural language processing libraries and sentiment analysis engines to analyze the tone of the user's voice and text in real time. Based on this analysis, it provides information that takes the user's mental state into account, supporting the learning process. Specifically, sentiment analysis tools such as IBM Watson and AWS Comprehend are used. Furthermore, OpenAI GPT-3 is used as the generative AI model to provide sophisticated conversational responses.

[0699] Users interact with this system via their terminals and receive information about their work in an easily understandable format. For example, when a new factory operator is undergoing training, the system not only explains the operating procedures in detail but also provides encouragement and supplementary information when it senses the operator's anxiety. This allows operators to work with greater confidence.

[0700] An example of a prompt sentence to be input into the generating AI model is, "Please provide a gentle way to instruct an operator who is feeling stressed on the production line." In this way, the present invention leverages its technological advancements to contribute to increased operational efficiency and improved user learning processes.

[0701] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0702] Step 1:

[0703] The server automatically collects data from devices that store information related to business operations. The input at this stage is raw data stored in the information storage device, and the output is organized business-related data. The server periodically monitors the devices and automatically retrieves new data when available.

[0704] Step 2:

[0705] The server analyzes key elements from the collected data and automatically generates business migration documents. The organized business-related data obtained in Step 1 is used as input. Natural language processing libraries (e.g., SpaCy or NLTK) are used to identify important information, and the output is a business migration document in a format that is easy for new business personnel to understand.

[0706] Step 3:

[0707] The server analyzes past interaction records using a conversation generation algorithm. The input is the collected history of past conversations. Based on this history, it generates typical questions and corresponding answers, automatically creating a list of frequently asked questions as output. By using a generation AI model (e.g., GPT-3), the high quality of the generated responses is guaranteed.

[0708] Step 4:

[0709] The server analyzes the user's emotional state and adjusts the content and tone of the information provided in real time. Input is user voice and text data, and the system uses an emotion analysis engine (e.g., IBM Watson, AWS Comprehend) to evaluate the user's emotions. The output generates flexible instructions tailored to the user's emotional state. For example, if the user is stressed, the system provides simpler and easier-to-understand instructions.

[0710] Step 5:

[0711] Users access business migration documents and FAQs generated through their terminals and receive interactive training. Input is learning materials provided by the server, and output is the user's learning progress and feedback. Motivational content and supplementary information are also provided to address any anxieties or questions users may have during their learning process.

[0712] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0713] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0715] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0716] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0717] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0718] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0719] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0720] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0721] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0722] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0723] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0724] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0725] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0726] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0727] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0728] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0729] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0730] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0731] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0732] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0733] The following is further disclosed regarding the embodiments described above.

[0734] (Claim 1)

[0735] A means for automatically collecting business documents from information media that store such documents,

[0736] A means for extracting important information from the aforementioned collected materials and automatically generating business handover documents,

[0737] A means of automatically generating frequently asked questions and their answers by analyzing past dialogue records using a conversation generation model,

[0738] A means of providing interactive training to new employees and enabling them to learn work procedures,

[0739] A system that includes this.

[0740] (Claim 2)

[0741] The system according to claim 1, further comprising means for formatting the automatically generated business handover document according to a predetermined template.

[0742] (Claim 3)

[0743] The system according to claim 1, wherein the conversation generation model further includes means for providing individual responses based on real-time input from a user.

[0744] "Example 1"

[0745] (Claim 1)

[0746] A means of automatically collecting business information from an information storage device,

[0747] A means for analyzing important information from the aforementioned collected information and automatically generating business transition documents,

[0748] A means for automatically generating frequently asked questions and their answers by analyzing previous dialogue history using a conversation generation algorithm,

[0749] A means of providing interactive training to new employees and helping them learn work procedures,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] The system according to claim 1, further comprising means for formatting the automatically generated business transition documents according to a predetermined format.

[0753] (Claim 3)

[0754] The system according to claim 1, wherein the conversation generation algorithm further includes means for providing individual responses based on real-time input from a user.

[0755] "Application Example 1"

[0756] (Claim 1)

[0757] A device that automatically collects business documents from an information medium that stores such documents,

[0758] A device that extracts important information from the aforementioned collected materials and automatically generates business handover documents,

[0759] A device that uses dialogue generation technology to analyze past dialogue records and automatically generate frequently asked questions and their answers,

[0760] A device that provides interactive training to new employees and helps them learn work procedures,

[0761] A display and audio guide device for visually and audibly presenting work procedures and answers to questions,

[0762] A device that provides work support through an information device,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, further comprising means for formatting the automatically generated business handover document according to a predetermined template and displaying it in a format suitable for an information device.

[0766] (Claim 3)

[0767] The system according to claim 1, wherein the dialogue generation technology further includes means for providing individual responses based on real-time input from a user and communicating them to an operator via an information device.

[0768] "Example 2 of combining an emotion engine"

[0769] (Claim 1)

[0770] A means of automatically collecting documents from information media,

[0771] A means for analyzing important data from the aforementioned collected information and automatically generating business handover documents,

[0772] A means of analyzing past dialogue history and using a generative model to automatically generate frequently occurring inquiries and their responses,

[0773] A means of providing users with individually tailored, interactive learning and enabling them to master procedures,

[0774] A means of analyzing the user's emotional state and adaptively adjusting the tone and content of the data provided,

[0775] A means of providing content to improve learning motivation based on the user's progress,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, further comprising means for formatting the automatically generated business handover documents according to a standardized format.

[0779] (Claim 3)

[0780] The system according to claim 1, wherein the generation model further includes means for providing individual responses based on input information.

[0781] "Application example 2 when combining with an emotional engine"

[0782] (Claim 1)

[0783] A function to automatically collect information from a device that stores information related to business operations,

[0784] The system analyzes key elements from the collected information and automatically generates business transition documents.

[0785] The system uses a conversation generation algorithm to analyze past interaction records and automatically generate typical questions and their answers.

[0786] It provides individualized training and features that enable users to learn the necessary operations.

[0787] A function that analyzes the user's emotional state and adjusts the content and tone of the instructions provided,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, further comprising a function for formatting the automatically generated business transition document according to a pre-set format.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the conversation generation algorithm further includes a function to provide individualized responses based on real-time input from the user. [Explanation of Symbols]

[0793] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for automatically collecting business documents from information media that store such documents, A means for extracting important information from the aforementioned collected materials and automatically generating business handover documents, A means of automatically generating frequently asked questions and their answers by analyzing past dialogue records using a conversation generation model, A means of providing interactive training to new employees and enabling them to learn work procedures, A system that includes this.

2. The system according to claim 1, further comprising means for formatting the automatically generated business handover document according to a predetermined template.

3. The system according to claim 1, wherein the conversation generation model further includes means for providing individual responses based on real-time input from a user.

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