System

Abstract

A system is provided.SOLUTION: This system is provided with a means for automatically arranging task contents by using a generation AI, a means for automatically preparing a succession from a predecessor to a successor and a means for proposing a proper dealing method to a new task by referring to the past dealing history.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The purpose of this invention is to solve the problem of business handover that occurs with personnel transfers. Specifically, the purpose is to enable a successor to efficiently and accurately take over business and continue work in situations where handover tends to be insufficient due to sudden transfers or the absence of the predecessor. It is also necessary to solve the case where the predecessor has difficulty preparing sufficient handover materials during busy work hours. [Means for solving the problem]

[0005] The present invention is a system that includes a means for automatically organizing work content using a generation AI, a means for automatically creating a handover document from a predecessor to a successor, and a means for proposing appropriate ways to handle new work by referring to past response history. Furthermore, the system includes a means for collecting the work content of the predecessor, and a means for recording and saving the generated work manual and handover document. This effectively solves the handover problem during personnel transfers and improves the stability and efficiency of work.

[0006] "Generative AI" refers to an artificial intelligence model that has self-learning and text-generating capabilities and generates appropriate output information based on specific input information.

[0007] "Job Description" refers to work activities, including all tasks, tasks, and responsibilities associated with a particular job or role.

[0008] "Organization" refers to the systematic classification and arrangement of multiple elements or information.

[0009] A "handover document" refers to a document in which a predecessor provides their successor with instructions on how to proceed with work and important information.

[0010] "Response history" refers to detailed records of past responses and actions, and the stored data.

[0011] A "proposal" refers to presenting a solution or recommended action for a problem or issue.

[0012] A "business manual" refers to a document that describes procedures and guidelines for carrying out specific tasks.

[0013] "Collection" refers to the gathering of specific information or data.

[0014] "Recording" refers to storing information or data and keeping it for future reference.

[0015] "Preservation" refers to the proper maintenance of information or data to protect it from loss or corruption. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0038] System configuration and operation

[0039] 1. Collecting business details

[0040] The user instructs the server to collect related business details.

[0041] The server collects work tasks from users' emails, task management tools, etc. For example, tasks such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system" are collected.

[0042] The collected business tasks are stored in a database on the server.

[0043] 2. Organizing work content and creating manuals

[0044] The server uses generative AI to organize the work content based on the collected business tasks.

[0045] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[0046] 3. Automatic creation of handover documents

[0047] The server automatically creates a handover document based on the generated business manual.

[0048] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[0049] 4. Responding to user questions

[0050] If the user has any questions about a new business, he or she asks the server a question.

[0051] The server references a database of previously created business manuals and handover documents and uses AI to generate appropriate answers.

[0052] The generated answer is provided to the user, and a response method is suggested.

[0053] Specific operation example

[0054] 1. Collecting business details

[0055] The user (Sato) requests the server to collect business tasks.

[0056] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[0057] 2. Organizing work content and creating manuals

[0058] Based on the collected tasks, the server uses a generation AI to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system."

[0059] The generated business manual is stored on the server.

[0060] 3. Automatic creation of handover documents

[0061] The server uses the generated business manual to automatically create a detailed handover document.

[0062] The handover document contains the necessary work content, how to carry it out, important points to note, contact information, etc., and is saved on the server. This handover document is provided when the work is handed over to a successor (for example, Tanaka).

[0063] 4. Responding to user questions

[0064] The user (Tanaka) asks the server, "Please tell me the format of the monthly report."

[0065] The server refers to a database of past business manuals and handover documents and uses a generation AI to generate an appropriate answer regarding the "monthly report format."

[0066] The generated answers are provided to Tanaka to guide him in creating an appropriate report.

[0067] In this way, the present invention provides a system that allows for smooth work handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0071] Step 2:

[0072] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[0073] Step 3:

[0074] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[0075] Step 4:

[0076] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0077] Step 5:

[0078] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[0079] Step 6:

[0080] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[0081] Step 7:

[0082] The server provides the generated answer to the user, for example, by informing the user, "Here is the format for the monthly report," and providing specific formatting guidelines.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In the conventional business handover process, the predecessor must manually organize the business content and create manuals and handover documents, which is time-consuming and labor-intensive, and there is a high possibility of gaps or inconsistencies in business knowledge. Furthermore, even after the business handover, it is difficult for the new employee to easily refer to past response history and learn appropriate response methods.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate response method for a new work task by referring to past response history, means for collecting work tasks based on user instructions, means for saving the collected work tasks in a database, means for generating a work manual using a generation AI based on the work tasks saved in the database, means for automatically creating a handover document based on the generated work manual, and means for generating appropriate answers to user questions. This streamlines the work handover process, reduces knowledge gaps and inconsistencies from the predecessor to the successor, and enables the successor to easily refer to past response history to obtain an appropriate response method.

[0088] "Generative AI" refers to artificial intelligence models that automatically create natural language text and other outputs generated based on user input and data.

[0089] "Means for automatically organizing work content" refers to the method or process by which the system processes the work tasks and data collected by the server and automatically organizes and classifies them using generative AI.

[0090] "Means for automatically creating handover documents" refers to a method or process in which a server uses generative AI or other algorithms to automatically organize work content and procedures and create handover documents.

[0091] "Means of proposing appropriate response methods for new tasks by referring to past response history" refers to a method or process for proposing appropriate response methods for new tasks by using generative AI or search algorithms based on data and manuals that the server has collected and stored in the past.

[0092] A "means for collecting business tasks based on user instructions" is a method or process by which a user instructs a server to collect business tasks through the system, and the server collects the necessary information from the user's relevant data sources based on that instruction.

[0093] "Means for storing collected business tasks in a database" refers to a method or process by which the server stores collected business tasks and data in a database and makes them available for later processing or reference.

[0094] "Means for generating business manuals using generative AI based on business tasks stored in a database" refers to a method or process by which a server reads business task data stored in a database and automatically creates detailed business manuals using generative AI.

[0095] "Means for automatically creating a handover document based on a generated business manual" refers to a method or process by which a server automatically creates a handover document by referring to the business manual created by the generation AI.

[0096] "Means for generating appropriate answers to user questions" refers to a method or process by which a server receives a question from a user, automatically generates an appropriate answer using a generation AI, and provides it to the user.

[0097] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0098] System configuration and operation overview

[0099] This system consists of a server, terminals, and users, and uses the following specific hardware and software:

[0100] Server: Processes and stores data (e.g., cloud server, on-premise server).

[0101] Terminal: A device used by a user to perform operations (e.g., PC, smartphone).

[0102] Database: A database management system such as PostgreSQL.

[0103] Generative AI models: Natural language generation models such as OpenAI's GPT-3.

[0104] API: An interface for connecting user data sources (email, task management tools) with the server (e.g., Microsoft Outlook API, Trello API).

[0105] Details of data processing and calculation

[0106] 1. Collecting business details

[0107] The user instructs the server to collect business tasks from the terminal, for example, by clicking the "Start collecting business tasks" button on the web dashboard.

[0108] The server collects business tasks from the user's mailbox and task management tool. Specifically, it uses the Microsoft Outlook API to obtain task information such as "Meeting with Client A" and "Preparing monthly report" from the mailbox.

[0109] The server stores the collected business tasks in a PostgreSQL database, along with information about each task (such as the task name, scheduled execution date, and person in charge).

[0110] 2. Organizing work content and creating manuals

[0111] The server uses generative AI (OpenAI's GPT-3) to organize tasks based on the business tasks stored in the database. For example, for the task "Preparing a monthly report," it extracts the subtasks "Data collection," "Report preparation," and "Review."

[0112] The server generates detailed work manuals based on the organized tasks. It specifically describes the procedures and important points for each task. For example, the "procedure for creating monthly reports" section includes the format template to be used and the specific method for collecting data.

[0113] The server saves the generated business manual in a database in Markdown format. For example, it saves it as "Monthly report creation procedure.md".

[0114] 3. Automatic creation of handover documents

[0115] The server automatically generates a handover document based on the generated business manual. The handover document includes information such as the job content, procedures, important points, and contact information. For example, it generates a section called "Important points to convey to the next successor."

[0116] The server saves the generated handover document in the database in Markdown format. For example, it saves it as "Handover_ClientA_Meeting.md".

[0117] 4. Responding to user questions

[0118] The user inputs a question to the server from the terminal, for example, "What is the format of the monthly report?"

[0119] The server searches the database for relevant business manuals and handover documents and generates appropriate answers using generation AI. For example, it extracts format information from "Monthly Report Creation Procedure.md."

[0120] The server provides the generated answer to the user, for example, "The format for the monthly report is as follows:"

[0121] Specific operation example

[0122] 1. The user (Sato) clicks the "Start collecting business tasks" button on the web dashboard.

[0123] 2. The server uses the Microsoft Outlook API to collect tasks such as "Meeting with Client A" from Sato's mailbox and save them in a database.

[0124] 3. The server uses GPT-3 to break down the task of "preparing monthly reports" into the subtasks of "data collection," "report preparation," and "review," and generates a manual.

[0125] 4. The server saves the manual as "Monthly Report Creation Procedure.md" and also generates a handover document.

[0126] 5. When a user (Tanaka) asks, "Please tell me the format of the monthly report," the server uses GPT-3 to generate an answer and provides it to Tanaka.

[0127] Prompt Sentence Examples

[0128] When a user asks a question to the server, the prompt is:

[0129] "Please tell me the format of the monthly report."

[0130] In this way, the present invention provides a system that allows for smooth business handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] User Instructions and Data Collection

[0134] Users access the system from a terminal (e.g., PC or smartphone) and instruct the server to collect business tasks. Specifically, collection begins by clicking the "Start business task collection" button on the web dashboard.

[0135] Input: User's instruction to start collection

[0136] The server receives instructions from the user and collects work tasks from the user's mailbox (e.g., using the Microsoft Outlook API) or task management tool (e.g., using the Trello API). For example, it retrieves schedules such as "Meeting with Client A" via the Microsoft Outlook API.

[0137] Output: Collected work task data

[0138] Specific operation: Using the Microsoft Outlook API, the server retrieves the latest business task information from the user's mailbox and saves the information for each task (task name, scheduled execution date, person in charge, etc.) in a database.

[0139] Step 2:

[0140] Data storage

[0141] The server stores the collected business tasks in a PostgreSQL database.

[0142] Input: Collected work task data

[0143] Output: Business tasks stored in a database

[0144] Specific operation: The server inserts the collected business task information into the Task table and assigns an ID so that each task can be uniquely identified. For example, "Meeting with Client A" is saved as "task_id=101".

[0145] Step 3:

[0146] Task organization

[0147] The server organizes tasks using generative AI (e.g., OpenAI's GPT-3) based on the business tasks stored in the database.

[0148] Input: Business task data stored in the database

[0149] Output: Organized task information

[0150] Specific operation: The server uses a generation AI to extract subtasks for the business task "Preparing monthly reports" - "Data collection," "Report preparation," and "Review." The importance and priority of the generated subtasks are then set.

[0151] Step 4:

[0152] Manual Generation

[0153] The server generates a detailed work manual based on the organized tasks, specifically describing the procedures and important points for each task.

[0154] Input: Organized task information

[0155] Output: Detailed business manual

[0156] Specific operation: The server uses the generation AI to describe in detail the "procedures for creating monthly reports." For example, the template to be used, the data collection method, review points, etc. are written in Markdown format. The generated business manual is saved in the database as "manual_id=201."

[0157] Step 5:

[0158] Automatic creation of handover documents

[0159] The server automatically creates a handover document based on the generated business manual.

[0160] Input: Detailed business manual

[0161] Output: Handover document

[0162] Specific operation: The server uses the generation AI to extract important points and points to note from each business manual and saves them in Markdown format as "Handover_ClientA_Meeting.md." The handover document contains detailed information about the specific procedures and contact information for each business.

[0163] Step 6:

[0164] Responding to user questions

[0165] The user inputs a question to the server from the terminal, such as "Please tell me the format of the monthly report."

[0166] Input: User question

[0167] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0168] Output: The generated answer

[0169] Specific operation: The server searches the database for "Monthly report creation procedure.md" and uses a generative AI (e.g., GPT-3) to generate a specific answer regarding "monthly report format." The generated answer is then provided to Tanaka, who provides specific guidance such as "The format of the monthly report is as follows:"

[0170] In this way, efficient business handover can be achieved through the specific actions and data flow at each step.

[0171] (Application example 1)

[0172] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0173] The handover of work tasks when introducing or maintaining a factory robot is complex and time-consuming. To efficiently take over work tasks, it is important to accurately understand the work of the predecessor and provide the new operator with appropriate manuals and procedures. However, traditional manual handovers are error-prone and take a lot of time and effort.

[0174] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0175] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate method for handling a new task by referring to past response history, means for managing work tasks by collecting the operation history, work logs, and sensor data of the factory robot, and means for generating a work manual based on the collected robot work content, thereby enabling quick and accurate handover of work for factory robots.

[0176] "Generative AI" refers to generative interactive artificial intelligence, a technology that performs tasks such as organizing work content, generating manuals, and providing automatic responses.

[0177] "Work content" refers to the tasks and work associated with a particular job or process.

[0178] A "handover document" is a document provided by a predecessor when handing over work to a successor, and it contains important details of the work, contact information, and how to respond.

[0179] "Response methods" refer to the methods or procedures that should be followed for a specific job or task.

[0180] A "factory robot" is a robot used to automate tasks and operations within a factory.

[0181] "Operation history" refers to historical information about the operation of robots and equipment.

[0182] A "work log" is a log that records the details of the work performed by a robot or device.

[0183] "Sensor data" refers to data acquired by a robot or device from built-in or external sensors.

[0184] A "work manual" is a detailed set of instructions for a specific work task.

[0185] A "server" is a computer system for storing, managing, and processing data.

[0186] This invention provides a system that uses generative AI to efficiently transfer tasks between factory robots. The system is mainly composed of a server, a robot, and a user.

[0187] System configuration and operation

[0188] 1. Collecting business details

[0189] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[0190] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[0191] 2. Organizing work content and creating manuals

[0192] The server uses generative AI to organize the work content based on the collected business tasks.

[0193] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[0194] 3. Automatic creation of handover documents

[0195] The server automatically creates a handover document based on the generated business manual.

[0196] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[0197] 4. Responding to user questions

[0198] If the user has any questions about a new business, he or she asks the server a question.

[0199] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0200] The generated answer is provided to the user, and a response method is suggested.

[0201] This system can be implemented using a programming language such as Python. The server uses a database management system (e.g., MySQL) and a generative AI model (e.g., OpenAI's GPT-3) to process each task. For example, for a factory robot's task of "assembling part A," the generated work manual would contain detailed instructions for "assembling part A."

[0202] Example prompt sentence:

[0203] Task: Create a detailed task manual based on Assemble part A.

[0204] This enables factory robots to take over tasks quickly and accurately.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] Collection of business details

[0208] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[0209] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[0210] Input: Instructions for collecting work content from the user

[0211] Output: Collected operation history, work logs, and sensor data are saved in a database

[0212] Step 2:

[0213] Organizing business operations

[0214] The server uses generative AI to organize the work content based on the collected business tasks.

[0215] Input: Business tasks stored in the database

[0216] Output: Tasks organized by the generative AI model

[0217] Specific operation: The server inputs the collected task data into a generative AI model (e.g., OpenAI GPT-3) and requests it to organize the work content. The generative AI analyzes the input data and organizes each task logically.

[0218] Step 3:

[0219] Generate business manuals

[0220] Based on the organized business content, the server uses generation AI to generate a detailed business manual.

[0221] Input: Tasks organized by the generative AI model

[0222] Output: A detailed business manual is generated.

[0223] Specific operation: The server inputs a prompt sentence into the AI ​​model based on the organized work content to generate a specific work manual. For example, the prompt sentence "Please generate a detailed work manual based on the work content: Assemble part A." is used.

[0224] Step 4:

[0225] Automatic creation of handover documents

[0226] The server automatically creates a handover document based on the generated business manual.

[0227] Input: Generated operations manual

[0228] Output: Generated handover document

[0229] Specific operation: Based on the contents of the generated business manual, the server automatically compiles a handover document that includes important business details, response methods, contact information, etc.

[0230] Step 5:

[0231] Responding to user questions

[0232] If the user has any questions about a new business, he or she asks the server a question.

[0233] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0234] Input: User question

[0235] Output: The correct answer generated

[0236] Specific operation: The server searches the database for relevant business manuals and handover documents based on the question, and inputs the question as a prompt into the generative AI model. The generative AI then generates an appropriate answer to the question and provides it to the user.

[0237] These steps ensure fast and accurate task handover for factory robots.

[0238] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0239] This invention provides a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, as well as a system that combines an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[0240] System configuration and operation

[0241] 1. Collecting business details

[0242] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0243] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[0244] 2. Organizing work content and creating manuals

[0245] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[0246] 3. Automatic creation of handover documents

[0247] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0248] 4. Emotion Recognition by Emotion Engine

[0249] The server uses an emotion engine to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[0250] The emotional states recognized by the emotion engine are recorded and stored in a database within the server.

[0251] 5. Adjusting work suggestions based on emotional state

[0252] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0253] 6. Responding to user questions

[0254] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[0255] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[0256] 7. Enhanced recommendations with an emotion engine

[0257] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[0258] Specific operation example

[0259] 1. Collecting business details

[0260] The user (Sato) requests the server to collect business tasks.

[0261] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[0262] 2. Organizing work content and creating manuals

[0263] The server uses a generation AI based on the collected tasks to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system." The generated business manuals are stored on the server.

[0264] 3. Automatic creation of handover documents

[0265] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[0266] 4. Emotion recognition and recording using the emotion engine

[0267] The server recognizes the emotions of the user who is taking over the task in real time. For example, if the user (Tanaka) is feeling stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[0268] 5. Adjusting work suggestions based on emotional state

[0269] The server recognizes that Tanaka is under stress and suggests only presenting high-priority tasks or postponing some tasks.

[0270] 6. Responding to user questions

[0271] A user (Tanaka) asks, "Please tell me the format of the monthly report."

[0272] The server references a database of past work manuals and handover documents and uses a generative AI to generate an appropriate answer regarding the "monthly report format." The answer is presented to Tanaka, who provides specific formatting guidelines.

[0273] 7. Enhanced recommendations with an emotion engine

[0274] The server considers the user's (Tanaka's) fatigue level and proposes measures to reduce the workload, such as suggesting that Tanaka take a short break, in order to improve work efficiency.

[0275] In this way, the present invention provides a system that uses generative AI and an emotion engine to smoothly carry out work handovers during personnel transfers by organizing work, generating manuals, creating handover documents, and responding to questions. By incorporating the user's emotional state, it becomes possible to make more human-friendly work proposals, with the aim of further improving the stability and efficiency of work.

[0276] The processing flow will be explained below.

[0277] Step 1:

[0278] The user requests the server to collect work tasks. Specifically, the user sends an instruction from the terminal to the server saying, "I want work tasks collected."

[0279] Step 2:

[0280] The server collects work tasks from the user's mailbox and task management tool. For example, the server automatically obtains task information such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system," and stores it in a database on the server.

[0281] Step 3:

[0282] The server uses a generation AI to organize the work content based on the collected task information. The server then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," "How to update the CRM system," etc.

[0283] Step 4:

[0284] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0285] Step 5:

[0286] The server uses an emotion engine to recognize the emotions of the user in real time while the server takes over the task. The server analyzes the user's emotional state (e.g., stress, excitement, fatigue) based on the text or voice information the user inputs through the terminal.

[0287] Step 6:

[0288] The emotional state of the user recognized by the emotion engine is recorded and stored in a database in the server.

[0289] Step 7:

[0290] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0291] Step 8:

[0292] The user sends a question to the server about a task that he or she is unsure about. Specifically, the user sends a question from the terminal, such as "Please tell me the format of the monthly report."

[0293] Step 9:

[0294] The server references a database of past work manuals and handover documents and uses AI to generate appropriate answers.The server prepares specific answers regarding the "monthly report format" based on past response history and stores them on the server.

[0295] Step 10:

[0296] The server provides the generated answer to the user, for example, notifying the user of the format of the monthly report and providing specific guidance.

[0297] Step 11:

[0298] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, the server will suggest ways to reduce the workload. Specifically, it will suggest the user take a short break to improve work efficiency.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In conventional task handover systems, the handover from predecessor to successor is done manually, which is time-consuming and labor-intensive, and the content of the handover is often subjective and inconsistent. Furthermore, because the system does not take the user's emotional state into consideration, there is a problem with task proposals that do not fully address the user's stress or fatigue. As a result, tasks can lack efficiency and accuracy. To solve these issues, a system is needed that can efficiently organize task content and create handover documents, as well as flexibly propose tasks based on the user's emotional state.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing appropriate ways of handling new work by referring to past response history, means for including an emotion engine for analyzing the user's emotions and adjusting the proposed work content based on the user's emotional state, and means for generating appropriate answers to user questions by referring to a past database using a generation AI. This makes it possible to efficiently and accurately organize work content, create a handover document, and make flexible work proposals and respond to questions while taking the user's emotional state into consideration.

[0304] "Generative AI" is a system that automatically generates text and data using artificial intelligence technology.

[0305] "Work organization" is the process of systematically analyzing a collection of complex work tasks and converting them into an easily understandable format.

[0306] A "handover document" is a document that summarizes the job content, procedures, important matters, etc. from the predecessor to the successor.

[0307] "Past response history" refers to records of previously performed tasks and responses.

[0308] An "appropriate response method" is the most effective and efficient way to respond to a task or problem.

[0309] An "emotion engine" is a system for analyzing and identifying a user's emotional state.

[0310] An "emotional state" is the mental and emotional state a user feels in response to a particular situation or environment.

[0311] "Adjusting task proposals" refers to changing the priority and order of tasks based on the user's emotional state, etc.

[0312] "Answers to questions" are solutions or information provided to users' questions or problems.

[0313] A "database" is a system for systematically storing, managing, and searching specific data.

[0314] A "server" is a computer system that provides data and services over a network.

[0315] "User" means the end user of this system.

[0316] "Means" are methods, tools, techniques, etc. used to achieve a particular goal or objective.

[0317] This invention is a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, and also combines it with an emotion engine that recognizes user emotions. This system is mainly composed of a server, a terminal, and a user.

[0318] System configuration and operation

[0319] Collection of business details

[0320] The user requests the server to collect work tasks. The server collects the work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello) and stores them in a database. For example, it extracts and stores task information such as "meeting with a client," "creating a monthly report," and "updating the CRM system."

[0321] Organizing work content and creating manuals

[0322] The server inputs the collected task information into a generation AI (e.g., OpenAI GPT-3) to generate a specific business manual. For example, the generation AI receives the following prompt:

[0323] "Create a detailed operations manual based on the collected task information."

[0324] This generates documents such as "Procedures for preparing for meetings with clients," "Procedures for creating monthly reports," and "How to update the CRM system." The generated business manuals are stored on the server.

[0325] Automatic creation of handover documents

[0326] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, execution methods, important points to note, contact information, etc. The generated handover document is saved on the server and is ready to be provided to the successor.

[0327] Emotion recognition by emotion engine

[0328] The server analyzes the text and voice information entered by the user using an emotion engine (e.g., IBM Watson Emotion Analysis). For example, the server recognizes the user's emotional state, such as stress or fatigue, during the handover process and records it in a database within the server. The server also analyzes the text and voice information using the following prompt:

[0329] "Analyze and record the user's emotional state based on text and voice input."

[0330] Adjusting work suggestions based on emotional state

[0331] The server adjusts its task suggestions based on the user's recorded emotional state: for example, if the user is stressed, the server may present only high-priority tasks or suggest postponing some tasks.

[0332] Responding to user questions

[0333] The user (successor) asks the server a question about work. For example, "Please tell me the format of the monthly report." The server generates a specific answer by referring to the generation AI and past databases and presents it to the user. The specific prompt is as follows:

[0334] "Can you give me an example of a monthly report and the format?"

[0335] Enhanced recommendations with an emotion engine

[0336] The server then analyzes the user's emotional state again and strengthens its work suggestions. For example, if the user is fatigued, the emotion engine will instruct the server to "suggest a short break" to improve work efficiency.

[0337] Specific operation example

[0338] 1. The user requests the server to collect business tasks.

[0339] 2. The server collects task information from mailboxes and task management tools and stores it in a database.

[0340] 3. The server uses the generation AI to generate the business manual and stores it on the server.

[0341] 4. The server creates a detailed handover document based on the generated business manual and stores it on the server.

[0342] 5. The server analyzes the information entered by the user using an emotion engine and records the recognized emotional state in a database.

[0343] 6. The server adjusts and presents task suggestions to the user based on the user's emotional state.

[0344] 7. The user asks a question, and the server uses generative AI to generate an answer and presents it to the user.

[0345] 8. The server proposes measures to reduce the workload based on the user's emotional state, thereby improving work efficiency.

[0346] This provides a system that enables highly efficient task handover through task organization, manual generation, handover document creation, and question response. By incorporating the user's emotional state, it is possible to make more human-friendly task proposals, aiming to further improve the stability and efficiency of tasks.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Step 1:

[0349] The user requests the collection of work tasks. The user inputs the command "Collect work tasks" to the server. The server receives this input and begins collecting work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello). The collected task information (e.g., "Meeting with client," "Preparing monthly report," "Updating CRM system," etc.) is extracted and stored in a database on the server.

[0350] Step 2:

[0351] The server inputs data into the generative AI based on the task information collected in step 1. Specific task information is used as input. A generative AI model (e.g., OpenAI GPT-3) is used to generate detailed business manuals for each task. For example, the generative AI receives a prompt such as, "Please create a detailed business manual based on the collected task information." The output generated in response to this prompt is a business manual such as "Procedures for preparing for a meeting with a client," "Procedures for creating a monthly report," or "How to update the CRM system," which is stored on the server.

[0352] Step 3:

[0353] The server creates a handover document based on the business manual generated in step 2. First, it uses the business manual as input and automatically extracts and organizes important business details, execution methods, important points to note, contact information, etc. As a result of this data processing, a "handover document" is generated. The handover document contains detailed business details and is saved on the server.

[0354] Step 4:

[0355] The server sends the text and voice information entered by the user to an emotion engine (e.g., IBM Watson Emotion Analysis). The user's text and voice information are used as input, and the emotion engine analyzes them. The output of the emotion engine is the user's emotional state (e.g., stress, excitement, fatigue, etc.) and is recorded in a database on the server.

[0356] Step 5:

[0357] The server adjusts the task suggestions based on the user's emotional state recorded in step 4. Using the emotional state data as input, the server prioritizes the tasks based on this data. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks. The adjusted results are then presented to the user.

[0358] Step 6:

[0359] The user sends a business-related question to the server. For example, the user might input a question like, "What is the format of a monthly report?" The server receives this input and inputs the question into the generation AI. The prompt text is, "Please tell me an example of a monthly report and its format." The generation AI generates an appropriate answer, and this output is returned to the server. Finally, the appropriate answer is provided to the user.

[0360] Step 7:

[0361] The server then re-analyzes the user's emotional state and enhances its work suggestions. The emotional state data is used as input, and the server makes optimal work suggestions to the user based on the results of the emotion engine. For example, if the user is fatigued, the emotion engine outputs a command to the server saying, "Please suggest a short break." The server then provides this suggestion to the user, aiming to improve work efficiency.

[0362] Through these processing steps, the system can efficiently and accurately carry out task handover. Furthermore, by taking the user's emotional state into consideration, it is possible to propose tasks that are more human-friendly, thereby improving the stability and efficiency of tasks.

[0363] (Application example 2)

[0364] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] In systems aimed at improving work handover and work efficiency, suggestions that do not take into account the emotional state of employees can increase employee stress and fatigue. Furthermore, when employees ask questions about unclear work content, they may not receive appropriate answers quickly. To address these issues, a system that recognizes employees' emotions in real time and generates work suggestions and answers is needed.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0367] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate way to handle new work by referring to past response history, means for recognizing employee emotions in real time and adjusting work proposals based on this, means for generating appropriate answers when employees ask questions about work, and means for proposing measures to reduce workload and prioritization based on emotions. This enables work handover that takes into account the emotional state of employees and improves work efficiency.

[0368] "Generative AI" is artificial intelligence that automatically generates content based on collected data.

[0369] "Organizing work content" is the process of classifying collected work tasks and compiling detailed procedures and points to note.

[0370] "Automatic handover document creation" is a function that automatically creates a document detailing the work to be handed over from the predecessor to the successor.

[0371] "A means of proposing appropriate ways to respond to new tasks by referring to past response history" is a function that proposes the optimal response to new tasks based on data from past tasks.

[0372] "Emotion recognition" is the process of analyzing an employee's text or voice data to understand their emotional state.

[0373] "Adjustment of work proposals" is a function that changes the content and order of work presented based on the employee's emotional state.

[0374] "Generating answers to questions" is the process of providing the most appropriate answer based on past data when an employee asks a question about something they don't understand at work.

[0375] "Workload reduction measures" are methods for reducing workload by adjusting the amount and importance of work, taking into account the emotional state of employees.

[0376] This invention is a system that uses generative AI to automatically organize the work content in a logistics center and combines it with an emotion engine that recognizes the emotions of employees. This system is mainly composed of a server, terminals, and users.

[0377] System configuration and operation

[0378] 1. Collecting business details

[0379] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0380] The server collects business tasks from users' mailboxes and task management tools. This includes task information such as inventory checks, shipping, etc. The collected task information is stored in a database on the server.

[0381] 2. Organizing work content and creating manuals

[0382] The server uses a generative AI model (such as TextGenie) to organize the work content based on the collected task information. It then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "procedures for inventory entry and retrieval work," "procedures for inventory confirmation," and "procedures for shipping work."

[0383] 3. Automatic creation of handover documents

[0384] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc., and the generated handover document is saved on the server.

[0385] 4. Emotion Recognition by Emotion Engine

[0386] The server uses an emotion engine (e.g., EmotionEngine) to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[0387] 5. Adjusting work suggestions based on emotional state

[0388] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0389] 6. Responding to user questions

[0390] The user asks the server a question about a task they are unsure about. The server then references a database of past task manuals and handover documents and uses a generative AI model to generate an appropriate answer. This allows the user to quickly obtain a specific answer.

[0391] 7. Enhanced recommendations with an emotion engine

[0392] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[0393] Specific operation example

[0394] Collection of business details

[0395] The user requests the server to collect business tasks.

[0396] The server collects tasks such as "warehouse entry and exit operations," "inventory checks," and "shipping operations" from users' mailboxes and task management tools and stores them in a database.

[0397] Organizing work content and creating manuals

[0398] The server uses AI generation to generate detailed business manuals based on the collected tasks, such as "procedures for receiving and retrieving goods," "procedures for inventory checks," and "procedures for shipping work." The generated business manuals are stored on the server.

[0399] Automatic creation of handover documents

[0400] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[0401] Emotion recognition and recording by emotion engine

[0402] The server recognizes the emotions of the user in real time during the handover process. For example, if the user feels stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[0403] Adjusting work suggestions based on emotional state

[0404] The server recognizes that the user is under stress and presents only high-priority tasks or suggests postponing some tasks.

[0405] Responding to user questions

[0406] The user asks, "Please tell me the procedure for loading and unloading."

[0407] The server references a database of past work manuals and handover documents, and uses a generative AI to generate an appropriate answer regarding "procedures for warehousing and shipping work." The answer is presented to the user, providing specific procedural guidelines.

[0408] Enhanced recommendations with an emotion engine

[0409] The server considers the user's level of fatigue and suggests ways to reduce the workload, such as suggesting that the user take a short break, in order to improve work efficiency.

[0410] Prompt Sentence Examples

[0411] By inputting the following prompt sentence into the generative AI model, the necessary business manual will be generated.

[0412] Please generate an operational manual for a logistics center. The collected tasks are as follows:

[0413] 1. Incoming and outgoing work

[0414] 2. Check inventory

[0415] 3. Shipping

[0416] Include detailed steps and notes for each task.

[0417] In this way, by using the system of the present invention, it is possible to significantly improve the efficiency of work within a logistics center. In addition, by making suggestions that take into account the emotional state of employees, it is possible to reduce the burden on employees and provide a comfortable working environment.

[0418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] The user requests the server to collect business tasks. The user sends an instruction to the server via their terminal to "collect business tasks." Upon receiving this input, the server accesses the configured mailbox and task management tool to collect data on business tasks (e.g., warehousing and shipping work, inventory checks, shipping work). As an output, the collected task information is saved in the server's database.

[0421] Step 2:

[0422] The server organizes business tasks and generates a business manual that includes detailed procedures and important points. Based on the collected task information, the server uses a generative AI model (e.g., textgenie) to classify business tasks and generate specific procedures for each task. For example, it might include "detailed procedures for inventory entry and retrieval work" and "important points for inventory checks." Based on this input process, the organized business content is output as a text-format business manual and saved on the server.

[0423] Step 3:

[0424] The server automatically creates handover documents based on the generated business manuals. The server extracts important points from each business manual and automatically generates a series of handover documents. The handover documents include the job content, procedures, response methods, contact information, etc., and are recorded in the server's database. The generated handover documents are then ready to be provided to the new employee.

[0425] Step 4:

[0426] The server recognizes the user's emotional state in real time. The user inputs emotional data via text or voice through the device. The server analyzes this input data using an emotion engine (e.g., EmotionEngine) to determine the user's emotional state (e.g., stress, excitement, fatigue). This emotional state data is stored on the server.

[0427] Step 5:

[0428] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server will suggest only high-priority tasks or postpone some tasks. Based on this adjustment suggestion, a task priority list is generated and sent to the user's device.

[0429] Step 6:

[0430] The user requests a question about work from the server. The user sends a specific question (e.g., "Please tell me the procedure for warehouse entry and exit work") to the server via their device. The server receives this input data, references a database of past work manuals and handover documents, and uses a generative AI model to generate the optimal answer. The generated answer is sent to the user's device in text format.

[0431] Step 7:

[0432] The server takes the user's emotional state into account to make more appropriate work suggestions. The server reanalyzes the emotional data and, if the user indicates fatigue, generates work load reduction measures, such as suggesting a short break. These specific suggestions are displayed in text format on the user's device.

[0433] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0439] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0441] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0443] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0444] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0445] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0446] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0448] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0449] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0450] System configuration and operation

[0451] 1. Collecting business details

[0452] The user instructs the server to collect related business details.

[0453] The server collects work tasks from users' emails, task management tools, etc. For example, tasks such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system" are collected.

[0454] The collected business tasks are stored in a database on the server.

[0455] 2. Organizing work content and creating manuals

[0456] The server uses generative AI to organize the work content based on the collected business tasks.

[0457] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[0458] 3. Automatic creation of handover documents

[0459] The server automatically creates a handover document based on the generated business manual.

[0460] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[0461] 4. Responding to user questions

[0462] If the user has any questions about a new business, he or she asks the server a question.

[0463] The server references a database of previously created business manuals and handover documents and uses AI to generate appropriate answers.

[0464] The generated answer is provided to the user, and a response method is suggested.

[0465] Specific operation example

[0466] 1. Collecting business details

[0467] The user (Sato) requests the server to collect business tasks.

[0468] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[0469] 2. Organizing work content and creating manuals

[0470] Based on the collected tasks, the server uses a generation AI to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system."

[0471] The generated business manual is stored on the server.

[0472] 3. Automatic creation of handover documents

[0473] The server uses the generated business manual to automatically create a detailed handover document.

[0474] The handover document contains the necessary work content, how to carry it out, important points to note, contact information, etc., and is saved on the server. This handover document is provided when the work is handed over to a successor (for example, Tanaka).

[0475] 4. Responding to user questions

[0476] The user (Tanaka) asks the server, "Please tell me the format of the monthly report."

[0477] The server refers to a database of past business manuals and handover documents and uses a generation AI to generate an appropriate answer regarding the "monthly report format."

[0478] The generated answers are provided to Tanaka to guide him in creating an appropriate report.

[0479] In this way, the present invention provides a system that allows for smooth work handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0480] The processing flow will be explained below.

[0481] Step 1:

[0482] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0483] Step 2:

[0484] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[0485] Step 3:

[0486] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[0487] Step 4:

[0488] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0489] Step 5:

[0490] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[0491] Step 6:

[0492] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[0493] Step 7:

[0494] The server provides the generated answer to the user, for example, by informing the user, "Here is the format for the monthly report," and providing specific formatting guidelines.

[0495] Example 1

[0496] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] In the conventional business handover process, the predecessor must manually organize the business content and create manuals and handover documents, which is time-consuming and labor-intensive, and there is a high possibility of gaps or inconsistencies in business knowledge. Furthermore, even after the business handover, it is difficult for the new employee to easily refer to past response history and learn appropriate response methods.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0499] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate response method for a new work task by referring to past response history, means for collecting work tasks based on user instructions, means for saving the collected work tasks in a database, means for generating a work manual using a generation AI based on the work tasks saved in the database, means for automatically creating a handover document based on the generated work manual, and means for generating appropriate answers to user questions. This streamlines the work handover process, reduces knowledge gaps and inconsistencies from the predecessor to the successor, and enables the successor to easily refer to past response history to obtain an appropriate response method.

[0500] "Generative AI" refers to artificial intelligence models that automatically create natural language text and other outputs generated based on user input and data.

[0501] "Means for automatically organizing work content" refers to the method or process by which the system processes the work tasks and data collected by the server and automatically organizes and classifies them using generative AI.

[0502] "Means for automatically creating handover documents" refers to a method or process in which a server uses generative AI or other algorithms to automatically organize work content and procedures and create handover documents.

[0503] "Means of proposing appropriate response methods for new tasks by referring to past response history" refers to a method or process for proposing appropriate response methods for new tasks by using generative AI or search algorithms based on data and manuals that the server has collected and stored in the past.

[0504] A "means for collecting business tasks based on user instructions" is a method or process by which a user instructs a server to collect business tasks through the system, and the server collects the necessary information from the user's relevant data sources based on that instruction.

[0505] "Means for storing collected business tasks in a database" refers to a method or process by which the server stores collected business tasks and data in a database and makes them available for later processing or reference.

[0506] "Means for generating business manuals using generative AI based on business tasks stored in a database" refers to a method or process by which a server reads business task data stored in a database and automatically creates detailed business manuals using generative AI.

[0507] "Means for automatically creating a handover document based on a generated business manual" refers to a method or process by which a server automatically creates a handover document by referring to the business manual created by the generation AI.

[0508] "Means for generating appropriate answers to user questions" refers to a method or process by which a server receives a question from a user, automatically generates an appropriate answer using a generation AI, and provides it to the user.

[0509] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0510] System configuration and operation overview

[0511] This system consists of a server, terminals, and users, and uses the following specific hardware and software:

[0512] Server: Processes and stores data (e.g., cloud server, on-premise server).

[0513] Terminal: A device used by a user to perform operations (e.g., PC, smartphone).

[0514] Database: A database management system such as PostgreSQL.

[0515] Generative AI models: Natural language generation models such as OpenAI's GPT-3.

[0516] API: An interface for connecting user data sources (email, task management tools) with the server (e.g., Microsoft Outlook API, Trello API).

[0517] Details of data processing and calculation

[0518] 1. Collecting business details

[0519] The user instructs the server to collect business tasks from the terminal, for example, by clicking the "Start collecting business tasks" button on the web dashboard.

[0520] The server collects business tasks from the user's mailbox and task management tool. Specifically, it uses the Microsoft Outlook API to obtain task information such as "Meeting with Client A" and "Preparing monthly report" from the mailbox.

[0521] The server stores the collected business tasks in a PostgreSQL database, along with information about each task (such as the task name, scheduled execution date, and person in charge).

[0522] 2. Organizing work content and creating manuals

[0523] The server uses generative AI (OpenAI's GPT-3) to organize tasks based on the business tasks stored in the database. For example, for the task "Preparing a monthly report," it extracts the subtasks "Data collection," "Report preparation," and "Review."

[0524] The server generates detailed work manuals based on the organized tasks. It specifically describes the procedures and important points for each task. For example, the "procedure for creating monthly reports" section includes the format template to be used and the specific method for collecting data.

[0525] The server saves the generated business manual in a database in Markdown format. For example, it saves it as "Monthly report creation procedure.md".

[0526] 3. Automatic creation of handover documents

[0527] The server automatically generates a handover document based on the generated business manual. The handover document includes information such as the job content, procedures, important points, and contact information. For example, it generates a section called "Important points to convey to the next successor."

[0528] The server saves the generated handover document in the database in Markdown format. For example, it saves it as "Handover_ClientA_Meeting.md".

[0529] 4. Responding to user questions

[0530] The user inputs a question to the server from the terminal, for example, "What is the format of the monthly report?"

[0531] The server searches the database for relevant business manuals and handover documents and generates appropriate answers using generation AI. For example, it extracts format information from "Monthly Report Creation Procedure.md."

[0532] The server provides the generated answer to the user, for example, "The format for the monthly report is as follows:"

[0533] Specific operation example

[0534] 1. The user (Sato) clicks the "Start collecting business tasks" button on the web dashboard.

[0535] 2. The server uses the Microsoft Outlook API to collect tasks such as "Meeting with Client A" from Sato's mailbox and save them in a database.

[0536] 3. The server uses GPT-3 to break down the task of "preparing monthly reports" into the subtasks of "data collection," "report preparation," and "review," and generates a manual.

[0537] 4. The server saves the manual as "Monthly Report Creation Procedure.md" and also generates a handover document.

[0538] 5. When a user (Tanaka) asks, "Please tell me the format of the monthly report," the server uses GPT-3 to generate an answer and provides it to Tanaka.

[0539] Prompt Sentence Examples

[0540] When a user asks a question to the server, the prompt is:

[0541] "Please tell me the format of the monthly report."

[0542] In this way, the present invention provides a system that allows for smooth business handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] User Instructions and Data Collection

[0546] Users access the system from a terminal (e.g., PC or smartphone) and instruct the server to collect business tasks. Specifically, collection begins by clicking the "Start business task collection" button on the web dashboard.

[0547] Input: User's instruction to start collection

[0548] The server receives instructions from the user and collects work tasks from the user's mailbox (e.g., using the Microsoft Outlook API) or task management tool (e.g., using the Trello API). For example, it retrieves schedules such as "Meeting with Client A" via the Microsoft Outlook API.

[0549] Output: Collected work task data

[0550] Specific operation: Using the Microsoft Outlook API, the server retrieves the latest business task information from the user's mailbox and saves the information for each task (task name, scheduled execution date, person in charge, etc.) in a database.

[0551] Step 2:

[0552] Data storage

[0553] The server stores the collected business tasks in a PostgreSQL database.

[0554] Input: Collected work task data

[0555] Output: Business tasks stored in a database

[0556] Specific operation: The server inserts the collected business task information into the Task table and assigns an ID so that each task can be uniquely identified. For example, "Meeting with Client A" is saved as "task_id=101".

[0557] Step 3:

[0558] Task organization

[0559] The server organizes tasks using generative AI (e.g., OpenAI's GPT-3) based on the business tasks stored in the database.

[0560] Input: Business task data stored in the database

[0561] Output: Organized task information

[0562] Specific operation: The server uses a generation AI to extract subtasks for the business task "Preparing monthly reports" - "Data collection," "Report preparation," and "Review." The importance and priority of the generated subtasks are then set.

[0563] Step 4:

[0564] Manual Generation

[0565] The server generates a detailed work manual based on the organized tasks, specifically describing the procedures and important points for each task.

[0566] Input: Organized task information

[0567] Output: Detailed business manual

[0568] Specific operation: The server uses the generation AI to describe in detail the "procedures for creating monthly reports." For example, the template to be used, the data collection method, review points, etc. are written in Markdown format. The generated business manual is saved in the database as "manual_id=201."

[0569] Step 5:

[0570] Automatic creation of handover documents

[0571] The server automatically creates a handover document based on the generated business manual.

[0572] Input: Detailed business manual

[0573] Output: Handover document

[0574] Specific operation: The server uses the generation AI to extract important points and points to note from each business manual and saves them in Markdown format as "Handover_ClientA_Meeting.md." The handover document contains detailed information about the specific procedures and contact information for each business.

[0575] Step 6:

[0576] Responding to user questions

[0577] The user inputs a question to the server from the terminal, such as "Please tell me the format of the monthly report."

[0578] Input: User question

[0579] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0580] Output: The generated answer

[0581] Specific operation: The server searches the database for "Monthly report creation procedure.md" and uses a generative AI (e.g., GPT-3) to generate a specific answer regarding "monthly report format." The generated answer is then provided to Tanaka, who provides specific guidance such as "The format of the monthly report is as follows:"

[0582] In this way, efficient business handover can be achieved through the specific actions and data flow at each step.

[0583] (Application example 1)

[0584] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0585] The handover of work tasks when introducing or maintaining a factory robot is complex and time-consuming. To efficiently take over work tasks, it is important to accurately understand the work of the predecessor and provide the new operator with appropriate manuals and procedures. However, traditional manual handovers are error-prone and take a lot of time and effort.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0587] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate method for handling a new task by referring to past response history, means for managing work tasks by collecting the operation history, work logs, and sensor data of the factory robot, and means for generating a work manual based on the collected robot work content, thereby enabling quick and accurate handover of work for factory robots.

[0588] "Generative AI" refers to generative interactive artificial intelligence, a technology that performs tasks such as organizing work content, generating manuals, and providing automatic responses.

[0589] "Work content" refers to the tasks and work associated with a particular job or process.

[0590] A "handover document" is a document provided by a predecessor when handing over work to a successor, and it contains important details of the work, contact information, and how to respond.

[0591] "Response methods" refer to the methods or procedures that should be followed for a specific job or task.

[0592] A "factory robot" is a robot used to automate tasks and operations within a factory.

[0593] "Operation history" refers to historical information about the operation of robots and equipment.

[0594] A "work log" is a log that records the details of the work performed by a robot or device.

[0595] "Sensor data" refers to data acquired by a robot or device from built-in or external sensors.

[0596] A "work manual" is a detailed set of instructions for a specific work task.

[0597] A "server" is a computer system for storing, managing, and processing data.

[0598] This invention provides a system that uses generative AI to efficiently transfer tasks between factory robots. The system is mainly composed of a server, a robot, and a user.

[0599] System configuration and operation

[0600] 1. Collecting business details

[0601] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[0602] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[0603] 2. Organizing work content and creating manuals

[0604] The server uses generative AI to organize the work content based on the collected business tasks.

[0605] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[0606] 3. Automatic creation of handover documents

[0607] The server automatically creates a handover document based on the generated business manual.

[0608] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[0609] 4. Responding to user questions

[0610] If the user has any questions about a new business, he or she asks the server a question.

[0611] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0612] The generated answer is provided to the user, and a response method is suggested.

[0613] This system can be implemented using a programming language such as Python. The server uses a database management system (e.g., MySQL) and a generative AI model (e.g., OpenAI's GPT-3) to process each task. For example, for a factory robot's task of "assembling part A," the generated work manual would contain detailed instructions for "assembling part A."

[0614] Example prompt sentence:

[0615] Task: Create a detailed task manual based on Assemble part A.

[0616] This enables factory robots to take over tasks quickly and accurately.

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1:

[0619] Collection of business details

[0620] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[0621] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[0622] Input: Instructions for collecting work content from the user

[0623] Output: Collected operation history, work logs, and sensor data are saved in a database

[0624] Step 2:

[0625] Organizing business operations

[0626] The server uses generative AI to organize the work content based on the collected business tasks.

[0627] Input: Business tasks stored in the database

[0628] Output: Tasks organized by the generative AI model

[0629] Specific operation: The server inputs the collected task data into a generative AI model (e.g., OpenAI GPT-3) and requests it to organize the work content. The generative AI analyzes the input data and organizes each task logically.

[0630] Step 3:

[0631] Generate business manuals

[0632] Based on the organized business content, the server uses generation AI to generate a detailed business manual.

[0633] Input: Tasks organized by the generative AI model

[0634] Output: A detailed business manual is generated.

[0635] Specific operation: The server inputs a prompt sentence into the AI ​​model based on the organized work content to generate a specific work manual. For example, the prompt sentence "Please generate a detailed work manual based on the work content: Assemble part A." is used.

[0636] Step 4:

[0637] Automatic creation of handover documents

[0638] The server automatically creates a handover document based on the generated business manual.

[0639] Input: Generated operations manual

[0640] Output: Generated handover document

[0641] Specific operation: Based on the contents of the generated business manual, the server automatically compiles a handover document that includes important business details, response methods, contact information, etc.

[0642] Step 5:

[0643] Responding to user questions

[0644] If the user has any questions about a new business, he or she asks the server a question.

[0645] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0646] Input: User question

[0647] Output: The correct answer generated

[0648] Specific operation: The server searches the database for relevant business manuals and handover documents based on the question, and inputs the question as a prompt into the generative AI model. The generative AI then generates an appropriate answer to the question and provides it to the user.

[0649] These steps ensure fast and accurate task handover for factory robots.

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] This invention provides a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, as well as a system that combines an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[0652] System configuration and operation

[0653] 1. Collecting business details

[0654] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0655] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[0656] 2. Organizing work content and creating manuals

[0657] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[0658] 3. Automatic creation of handover documents

[0659] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0660] 4. Emotion Recognition by Emotion Engine

[0661] The server uses an emotion engine to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[0662] The emotional states recognized by the emotion engine are recorded and stored in a database within the server.

[0663] 5. Adjusting work suggestions based on emotional state

[0664] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0665] 6. Responding to user questions

[0666] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[0667] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[0668] 7. Enhanced recommendations with an emotion engine

[0669] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[0670] Specific operation example

[0671] 1. Collecting business details

[0672] The user (Sato) requests the server to collect business tasks.

[0673] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[0674] 2. Organizing work content and creating manuals

[0675] The server uses a generation AI based on the collected tasks to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system." The generated business manuals are stored on the server.

[0676] 3. Automatic creation of handover documents

[0677] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[0678] 4. Emotion recognition and recording using the emotion engine

[0679] The server recognizes the emotions of the user who is taking over the task in real time. For example, if the user (Tanaka) is feeling stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[0680] 5. Adjusting work suggestions based on emotional state

[0681] The server recognizes that Tanaka is under stress and suggests only presenting high-priority tasks or postponing some tasks.

[0682] 6. Responding to user questions

[0683] A user (Tanaka) asks, "Please tell me the format of the monthly report."

[0684] The server references a database of past work manuals and handover documents and uses a generative AI to generate an appropriate answer regarding the "monthly report format." The answer is presented to Tanaka, who provides specific formatting guidelines.

[0685] 7. Enhanced recommendations with an emotion engine

[0686] The server considers the user's (Tanaka's) fatigue level and proposes measures to reduce the workload, such as suggesting that Tanaka take a short break, in order to improve work efficiency.

[0687] In this way, the present invention provides a system that uses generative AI and an emotion engine to smoothly carry out work handovers during personnel transfers by organizing work, generating manuals, creating handover documents, and responding to questions. By incorporating the user's emotional state, it becomes possible to make more human-friendly work proposals, with the aim of further improving the stability and efficiency of work.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] The user requests the server to collect work tasks. Specifically, the user sends an instruction from the terminal to the server saying, "I want work tasks collected."

[0691] Step 2:

[0692] The server collects work tasks from the user's mailbox and task management tool. For example, the server automatically obtains task information such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system," and stores it in a database on the server.

[0693] Step 3:

[0694] The server uses a generation AI to organize the work content based on the collected task information. The server then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," "How to update the CRM system," etc.

[0695] Step 4:

[0696] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0697] Step 5:

[0698] The server uses an emotion engine to recognize the emotions of the user in real time while the server takes over the task. The server analyzes the user's emotional state (e.g., stress, excitement, fatigue) based on the text or voice information the user inputs through the terminal.

[0699] Step 6:

[0700] The emotional state of the user recognized by the emotion engine is recorded and stored in a database in the server.

[0701] Step 7:

[0702] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0703] Step 8:

[0704] The user sends a question to the server about a task that he or she is unsure about. Specifically, the user sends a question from the terminal, such as "Please tell me the format of the monthly report."

[0705] Step 9:

[0706] The server references a database of past work manuals and handover documents and uses AI to generate appropriate answers.The server prepares specific answers regarding the "monthly report format" based on past response history and stores them on the server.

[0707] Step 10:

[0708] The server provides the generated answer to the user, for example, notifying the user of the format of the monthly report and providing specific guidance.

[0709] Step 11:

[0710] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, the server will suggest ways to reduce the workload. Specifically, it will suggest the user take a short break to improve work efficiency.

[0711] Example 2

[0712] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0713] In conventional task handover systems, the handover from predecessor to successor is done manually, which is time-consuming and labor-intensive, and the content of the handover is often subjective and inconsistent. Furthermore, because the system does not take the user's emotional state into consideration, there is a problem with task proposals that do not fully address the user's stress or fatigue. As a result, tasks can lack efficiency and accuracy. To solve these issues, a system is needed that can efficiently organize task content and create handover documents, as well as flexibly propose tasks based on the user's emotional state.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0715] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing appropriate ways of handling new work by referring to past response history, means for including an emotion engine for analyzing the user's emotions and adjusting the proposed work content based on the user's emotional state, and means for generating appropriate answers to user questions by referring to a past database using a generation AI. This makes it possible to efficiently and accurately organize work content, create a handover document, and make flexible work proposals and respond to questions while taking the user's emotional state into consideration.

[0716] "Generative AI" is a system that automatically generates text and data using artificial intelligence technology.

[0717] "Work organization" is the process of systematically analyzing a collection of complex work tasks and converting them into an easily understandable format.

[0718] A "handover document" is a document that summarizes the job content, procedures, important matters, etc. from the predecessor to the successor.

[0719] "Past response history" refers to records of previously performed tasks and responses.

[0720] An "appropriate response method" is the most effective and efficient way to respond to a task or problem.

[0721] An "emotion engine" is a system for analyzing and identifying a user's emotional state.

[0722] An "emotional state" is the mental and emotional state a user feels in response to a particular situation or environment.

[0723] "Adjusting task proposals" refers to changing the priority and order of tasks based on the user's emotional state, etc.

[0724] "Answers to questions" are solutions or information provided to users' questions or problems.

[0725] A "database" is a system for systematically storing, managing, and searching specific data.

[0726] A "server" is a computer system that provides data and services over a network.

[0727] "User" means the end user of this system.

[0728] "Means" are methods, tools, techniques, etc. used to achieve a particular goal or objective.

[0729] This invention is a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, and also combines it with an emotion engine that recognizes user emotions. This system is mainly composed of a server, a terminal, and a user.

[0730] System configuration and operation

[0731] Collection of business details

[0732] The user requests the server to collect work tasks. The server collects the work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello) and stores them in a database. For example, it extracts and stores task information such as "meeting with a client," "creating a monthly report," and "updating the CRM system."

[0733] Organizing work content and creating manuals

[0734] The server inputs the collected task information into a generation AI (e.g., OpenAI GPT-3) to generate a specific business manual. For example, the generation AI receives the following prompt:

[0735] "Create a detailed operations manual based on the collected task information."

[0736] This generates documents such as "Procedures for preparing for meetings with clients," "Procedures for creating monthly reports," and "How to update the CRM system." The generated business manuals are stored on the server.

[0737] Automatic creation of handover documents

[0738] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, execution methods, important points to note, contact information, etc. The generated handover document is saved on the server and is ready to be provided to the successor.

[0739] Emotion recognition by emotion engine

[0740] The server analyzes the text and voice information entered by the user using an emotion engine (e.g., IBM Watson Emotion Analysis). For example, the server recognizes the user's emotional state, such as stress or fatigue, during the handover process and records it in a database within the server. The server also analyzes the text and voice information using the following prompt:

[0741] "Analyze and record the user's emotional state based on text and voice input."

[0742] Adjusting work suggestions based on emotional state

[0743] The server adjusts its task suggestions based on the user's recorded emotional state: for example, if the user is stressed, the server may present only high-priority tasks or suggest postponing some tasks.

[0744] Responding to user questions

[0745] The user (successor) asks the server a question about work. For example, "Please tell me the format of the monthly report." The server generates a specific answer by referring to the generation AI and past databases and presents it to the user. The specific prompt is as follows:

[0746] "Can you give me an example of a monthly report and the format?"

[0747] Enhanced recommendations with an emotion engine

[0748] The server then analyzes the user's emotional state again and strengthens its work suggestions. For example, if the user is fatigued, the emotion engine will instruct the server to "suggest a short break" to improve work efficiency.

[0749] Specific operation example

[0750] 1. The user requests the server to collect business tasks.

[0751] 2. The server collects task information from mailboxes and task management tools and stores it in a database.

[0752] 3. The server uses the generation AI to generate the business manual and stores it on the server.

[0753] 4. The server creates a detailed handover document based on the generated business manual and stores it on the server.

[0754] 5. The server analyzes the information entered by the user using an emotion engine and records the recognized emotional state in a database.

[0755] 6. The server adjusts and presents task suggestions to the user based on the user's emotional state.

[0756] 7. The user asks a question, and the server uses generative AI to generate an answer and presents it to the user.

[0757] 8. The server proposes measures to reduce the workload based on the user's emotional state, thereby improving work efficiency.

[0758] This provides a system that enables highly efficient task handover through task organization, manual generation, handover document creation, and question response. By incorporating the user's emotional state, it is possible to make more human-friendly task proposals, aiming to further improve the stability and efficiency of tasks.

[0759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] The user requests the collection of work tasks. The user inputs the command "Collect work tasks" to the server. The server receives this input and begins collecting work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello). The collected task information (e.g., "Meeting with client," "Preparing monthly report," "Updating CRM system," etc.) is extracted and stored in a database on the server.

[0762] Step 2:

[0763] The server inputs data into the generative AI based on the task information collected in step 1. Specific task information is used as input. A generative AI model (e.g., OpenAI GPT-3) is used to generate detailed business manuals for each task. For example, the generative AI receives a prompt such as, "Please create a detailed business manual based on the collected task information." The output generated in response to this prompt is a business manual such as "Procedures for preparing for a meeting with a client," "Procedures for creating a monthly report," or "How to update the CRM system," which is stored on the server.

[0764] Step 3:

[0765] The server creates a handover document based on the business manual generated in step 2. First, it uses the business manual as input and automatically extracts and organizes important business details, execution methods, important points to note, contact information, etc. As a result of this data processing, a "handover document" is generated. The handover document contains detailed business details and is saved on the server.

[0766] Step 4:

[0767] The server sends the text and voice information entered by the user to an emotion engine (e.g., IBM Watson Emotion Analysis). The user's text and voice information are used as input, and the emotion engine analyzes them. The output of the emotion engine is the user's emotional state (e.g., stress, excitement, fatigue, etc.) and is recorded in a database on the server.

[0768] Step 5:

[0769] The server adjusts the task suggestions based on the user's emotional state recorded in step 4. Using the emotional state data as input, the server prioritizes the tasks based on this data. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks. The adjusted results are then presented to the user.

[0770] Step 6:

[0771] The user sends a business-related question to the server. For example, the user might input a question like, "What is the format of a monthly report?" The server receives this input and inputs the question into the generation AI. The prompt text is, "Please tell me an example of a monthly report and its format." The generation AI generates an appropriate answer, and this output is returned to the server. Finally, the appropriate answer is provided to the user.

[0772] Step 7:

[0773] The server then re-analyzes the user's emotional state and enhances its work suggestions. The emotional state data is used as input, and the server makes optimal work suggestions to the user based on the results of the emotion engine. For example, if the user is fatigued, the emotion engine outputs a command to the server saying, "Please suggest a short break." The server then provides this suggestion to the user, aiming to improve work efficiency.

[0774] Through these processing steps, the system can efficiently and accurately carry out task handover. Furthermore, by taking the user's emotional state into consideration, it is possible to propose tasks that are more human-friendly, thereby improving the stability and efficiency of tasks.

[0775] (Application example 2)

[0776] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0777] In systems aimed at improving work handover and work efficiency, suggestions that do not take into account the emotional state of employees can increase employee stress and fatigue. Furthermore, when employees ask questions about unclear work content, they may not receive appropriate answers quickly. To address these issues, a system that recognizes employees' emotions in real time and generates work suggestions and answers is needed.

[0778] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0779] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate way to handle new work by referring to past response history, means for recognizing employee emotions in real time and adjusting work proposals based on this, means for generating appropriate answers when employees ask questions about work, and means for proposing measures to reduce workload and prioritization based on emotions. This enables work handover that takes into account the emotional state of employees and improves work efficiency.

[0780] "Generative AI" is artificial intelligence that automatically generates content based on collected data.

[0781] "Organizing work content" is the process of classifying collected work tasks and compiling detailed procedures and points to note.

[0782] "Automatic handover document creation" is a function that automatically creates a document detailing the work to be handed over from the predecessor to the successor.

[0783] "A means of proposing appropriate ways to respond to new tasks by referring to past response history" is a function that proposes the optimal response to new tasks based on data from past tasks.

[0784] "Emotion recognition" is the process of analyzing an employee's text or voice data to understand their emotional state.

[0785] "Adjustment of work proposals" is a function that changes the content and order of work presented based on the employee's emotional state.

[0786] "Generating answers to questions" is the process of providing the most appropriate answer based on past data when an employee asks a question about something they don't understand at work.

[0787] "Workload reduction measures" are methods for reducing workload by adjusting the amount and importance of work, taking into account the emotional state of employees.

[0788] This invention is a system that uses generative AI to automatically organize the work content in a logistics center and combines it with an emotion engine that recognizes the emotions of employees. This system is mainly composed of a server, terminals, and users.

[0789] System configuration and operation

[0790] 1. Collecting business details

[0791] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0792] The server collects business tasks from users' mailboxes and task management tools. This includes task information such as inventory checks, shipping, etc. The collected task information is stored in a database on the server.

[0793] 2. Organizing work content and creating manuals

[0794] The server uses a generative AI model (such as TextGenie) to organize the work content based on the collected task information. It then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "procedures for inventory entry and retrieval work," "procedures for inventory confirmation," and "procedures for shipping work."

[0795] 3. Automatic creation of handover documents

[0796] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc., and the generated handover document is saved on the server.

[0797] 4. Emotion Recognition by Emotion Engine

[0798] The server uses an emotion engine (e.g., EmotionEngine) to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[0799] 5. Adjusting work suggestions based on emotional state

[0800] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[0801] 6. Responding to user questions

[0802] The user asks the server a question about a task they are unsure about. The server then references a database of past task manuals and handover documents and uses a generative AI model to generate an appropriate answer. This allows the user to quickly obtain a specific answer.

[0803] 7. Enhanced recommendations with an emotion engine

[0804] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[0805] Specific operation example

[0806] Collection of business details

[0807] The user requests the server to collect business tasks.

[0808] The server collects tasks such as "warehouse entry and exit operations," "inventory checks," and "shipping operations" from users' mailboxes and task management tools and stores them in a database.

[0809] Organizing work content and creating manuals

[0810] The server uses AI generation to generate detailed business manuals based on the collected tasks, such as "procedures for receiving and retrieving goods," "procedures for inventory checks," and "procedures for shipping work." The generated business manuals are stored on the server.

[0811] Automatic creation of handover documents

[0812] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[0813] Emotion recognition and recording by emotion engine

[0814] The server recognizes the emotions of the user in real time during the handover process. For example, if the user feels stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[0815] Adjusting work suggestions based on emotional state

[0816] The server recognizes that the user is under stress and presents only high-priority tasks or suggests postponing some tasks.

[0817] Responding to user questions

[0818] The user asks, "Please tell me the procedure for loading and unloading."

[0819] The server references a database of past work manuals and handover documents, and uses a generative AI to generate an appropriate answer regarding "procedures for warehousing and shipping work." The answer is presented to the user, providing specific procedural guidelines.

[0820] Enhanced recommendations with an emotion engine

[0821] The server considers the user's level of fatigue and suggests ways to reduce the workload, such as suggesting that the user take a short break, in order to improve work efficiency.

[0822] Prompt Sentence Examples

[0823] By inputting the following prompt sentence into the generative AI model, the necessary business manual will be generated.

[0824] Please generate an operational manual for a logistics center. The collected tasks are as follows:

[0825] 1. Incoming and outgoing work

[0826] 2. Check inventory

[0827] 3. Shipping

[0828] Include detailed steps and notes for each task.

[0829] In this way, by using the system of the present invention, it is possible to significantly improve the efficiency of work within a logistics center. In addition, by making suggestions that take into account the emotional state of employees, it is possible to reduce the burden on employees and provide a comfortable working environment.

[0830] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0831] Step 1:

[0832] The user requests the server to collect business tasks. The user sends an instruction to the server via their terminal to "collect business tasks." Upon receiving this input, the server accesses the configured mailbox and task management tool to collect data on business tasks (e.g., warehousing and shipping work, inventory checks, shipping work). As an output, the collected task information is saved in the server's database.

[0833] Step 2:

[0834] The server organizes business tasks and generates a business manual that includes detailed procedures and important points. Based on the collected task information, the server uses a generative AI model (e.g., textgenie) to classify business tasks and generate specific procedures for each task. For example, it might include "detailed procedures for inventory entry and retrieval work" and "important points for inventory checks." Based on this input process, the organized business content is output as a text-format business manual and saved on the server.

[0835] Step 3:

[0836] The server automatically creates handover documents based on the generated business manuals. The server extracts important points from each business manual and automatically generates a series of handover documents. The handover documents include the job content, procedures, response methods, contact information, etc., and are recorded in the server's database. The generated handover documents are then ready to be provided to the new employee.

[0837] Step 4:

[0838] The server recognizes the user's emotional state in real time. The user inputs emotional data via text or voice through the device. The server analyzes this input data using an emotion engine (e.g., EmotionEngine) to determine the user's emotional state (e.g., stress, excitement, fatigue). This emotional state data is stored on the server.

[0839] Step 5:

[0840] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server will suggest only high-priority tasks or postpone some tasks. Based on this adjustment suggestion, a task priority list is generated and sent to the user's device.

[0841] Step 6:

[0842] The user requests a question about work from the server. The user sends a specific question (e.g., "Please tell me the procedure for warehouse entry and exit work") to the server via their device. The server receives this input data, references a database of past work manuals and handover documents, and uses a generative AI model to generate the optimal answer. The generated answer is sent to the user's device in text format.

[0843] Step 7:

[0844] The server takes the user's emotional state into account to make more appropriate work suggestions. The server reanalyzes the emotional data and, if the user indicates fatigue, generates work load reduction measures, such as suggesting a short break. These specific suggestions are displayed in text format on the user's device.

[0845] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0846] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0848] [Third embodiment]

[0849] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0850] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0851] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0852] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0853] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0854] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0855] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0856] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0857] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0858] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0859] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0860] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0861] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0862] System configuration and operation

[0863] 1. Collecting business details

[0864] The user instructs the server to collect related business details.

[0865] The server collects work tasks from users' emails, task management tools, etc. For example, tasks such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system" are collected.

[0866] The collected business tasks are stored in a database on the server.

[0867] 2. Organizing work content and creating manuals

[0868] The server uses generative AI to organize the work content based on the collected business tasks.

[0869] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[0870] 3. Automatic creation of handover documents

[0871] The server automatically creates a handover document based on the generated business manual.

[0872] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[0873] 4. Responding to user questions

[0874] If the user has any questions about a new business, he or she asks the server a question.

[0875] The server references a database of previously created business manuals and handover documents and uses AI to generate appropriate answers.

[0876] The generated answer is provided to the user, and a response method is suggested.

[0877] Specific operation example

[0878] 1. Collecting business details

[0879] The user (Sato) requests the server to collect business tasks.

[0880] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[0881] 2. Organizing work content and creating manuals

[0882] Based on the collected tasks, the server uses a generation AI to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system."

[0883] The generated business manual is stored on the server.

[0884] 3. Automatic creation of handover documents

[0885] The server uses the generated business manual to automatically create a detailed handover document.

[0886] The handover document contains the necessary work content, how to carry it out, important points to note, contact information, etc., and is saved on the server. This handover document is provided when the work is handed over to a successor (for example, Tanaka).

[0887] 4. Responding to user questions

[0888] The user (Tanaka) asks the server, "Please tell me the format of the monthly report."

[0889] The server refers to a database of past business manuals and handover documents and uses a generation AI to generate an appropriate answer regarding the "monthly report format."

[0890] The generated answers are provided to Tanaka to guide him in creating an appropriate report.

[0891] In this way, the present invention provides a system that allows for smooth work handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0892] The processing flow will be explained below.

[0893] Step 1:

[0894] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[0895] Step 2:

[0896] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[0897] Step 3:

[0898] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[0899] Step 4:

[0900] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[0901] Step 5:

[0902] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[0903] Step 6:

[0904] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[0905] Step 7:

[0906] The server provides the generated answer to the user, for example, by informing the user, "Here is the format for the monthly report," and providing specific formatting guidelines.

[0907] Example 1

[0908] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0909] In the conventional business handover process, the predecessor must manually organize the business content and create manuals and handover documents, which is time-consuming and labor-intensive, and there is a high possibility of gaps or inconsistencies in business knowledge. Furthermore, even after the business handover, it is difficult for the new employee to easily refer to past response history and learn appropriate response methods.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0911] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate response method for a new work task by referring to past response history, means for collecting work tasks based on user instructions, means for saving the collected work tasks in a database, means for generating a work manual using a generation AI based on the work tasks saved in the database, means for automatically creating a handover document based on the generated work manual, and means for generating appropriate answers to user questions. This streamlines the work handover process, reduces knowledge gaps and inconsistencies from the predecessor to the successor, and enables the successor to easily refer to past response history to obtain an appropriate response method.

[0912] "Generative AI" refers to artificial intelligence models that automatically create natural language text and other outputs generated based on user input and data.

[0913] "Means for automatically organizing work content" refers to the method or process by which the system processes the work tasks and data collected by the server and automatically organizes and classifies them using generative AI.

[0914] "Means for automatically creating handover documents" refers to a method or process in which a server uses generative AI or other algorithms to automatically organize work content and procedures and create handover documents.

[0915] "Means of proposing appropriate response methods for new tasks by referring to past response history" refers to a method or process for proposing appropriate response methods for new tasks by using generative AI or search algorithms based on data and manuals that the server has collected and stored in the past.

[0916] A "means for collecting business tasks based on user instructions" is a method or process by which a user instructs a server to collect business tasks through the system, and the server collects the necessary information from the user's relevant data sources based on that instruction.

[0917] "Means for storing collected business tasks in a database" refers to a method or process by which the server stores collected business tasks and data in a database and makes them available for later processing or reference.

[0918] "Means for generating business manuals using generative AI based on business tasks stored in a database" refers to a method or process by which a server reads business task data stored in a database and automatically creates detailed business manuals using generative AI.

[0919] "Means for automatically creating a handover document based on a generated business manual" refers to a method or process by which a server automatically creates a handover document by referring to the business manual created by the generation AI.

[0920] "Means for generating appropriate answers to user questions" refers to a method or process by which a server receives a question from a user, automatically generates an appropriate answer using a generation AI, and provides it to the user.

[0921] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[0922] System configuration and operation overview

[0923] This system consists of a server, terminals, and users, and uses the following specific hardware and software:

[0924] Server: Processes and stores data (e.g., cloud server, on-premise server).

[0925] Terminal: A device used by a user to perform operations (e.g., PC, smartphone).

[0926] Database: A database management system such as PostgreSQL.

[0927] Generative AI models: Natural language generation models such as OpenAI's GPT-3.

[0928] API: An interface for connecting user data sources (email, task management tools) with the server (e.g., Microsoft Outlook API, Trello API).

[0929] Details of data processing and calculation

[0930] 1. Collecting business details

[0931] The user instructs the server to collect business tasks from the terminal, for example, by clicking the "Start collecting business tasks" button on the web dashboard.

[0932] The server collects business tasks from the user's mailbox and task management tool. Specifically, it uses the Microsoft Outlook API to obtain task information such as "Meeting with Client A" and "Preparing monthly report" from the mailbox.

[0933] The server stores the collected business tasks in a PostgreSQL database, along with information about each task (such as the task name, scheduled execution date, and person in charge).

[0934] 2. Organizing work content and creating manuals

[0935] The server uses generative AI (OpenAI's GPT-3) to organize tasks based on the business tasks stored in the database. For example, for the task "Preparing a monthly report," it extracts the subtasks "Data collection," "Report preparation," and "Review."

[0936] The server generates detailed work manuals based on the organized tasks. It specifically describes the procedures and important points for each task. For example, the "procedure for creating monthly reports" section includes the format template to be used and the specific method for collecting data.

[0937] The server saves the generated business manual in a database in Markdown format. For example, it saves it as "Monthly report creation procedure.md".

[0938] 3. Automatic creation of handover documents

[0939] The server automatically generates a handover document based on the generated business manual. The handover document includes information such as the job content, procedures, important points, and contact information. For example, it generates a section called "Important points to convey to the next successor."

[0940] The server saves the generated handover document in the database in Markdown format. For example, it saves it as "Handover_ClientA_Meeting.md".

[0941] 4. Responding to user questions

[0942] The user inputs a question to the server from the terminal, for example, "What is the format of the monthly report?"

[0943] The server searches the database for relevant business manuals and handover documents and generates appropriate answers using generation AI. For example, it extracts format information from "Monthly Report Creation Procedure.md."

[0944] The server provides the generated answer to the user, for example, "The format for the monthly report is as follows:"

[0945] Specific operation example

[0946] 1. The user (Sato) clicks the "Start collecting business tasks" button on the web dashboard.

[0947] 2. The server uses the Microsoft Outlook API to collect tasks such as "Meeting with Client A" from Sato's mailbox and save them in a database.

[0948] 3. The server uses GPT-3 to break down the task of "preparing monthly reports" into the subtasks of "data collection," "report preparation," and "review," and generates a manual.

[0949] 4. The server saves the manual as "Monthly Report Creation Procedure.md" and also generates a handover document.

[0950] 5. When a user (Tanaka) asks, "Please tell me the format of the monthly report," the server uses GPT-3 to generate an answer and provides it to Tanaka.

[0951] Prompt Sentence Examples

[0952] When a user asks a question to the server, the prompt is:

[0953] "Please tell me the format of the monthly report."

[0954] In this way, the present invention provides a system that allows for smooth business handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[0955] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0956] Step 1:

[0957] User Instructions and Data Collection

[0958] Users access the system from a terminal (e.g., PC or smartphone) and instruct the server to collect business tasks. Specifically, collection begins by clicking the "Start business task collection" button on the web dashboard.

[0959] Input: User's instruction to start collection

[0960] The server receives instructions from the user and collects work tasks from the user's mailbox (e.g., using the Microsoft Outlook API) or task management tool (e.g., using the Trello API). For example, it retrieves schedules such as "Meeting with Client A" via the Microsoft Outlook API.

[0961] Output: Collected work task data

[0962] Specific operation: Using the Microsoft Outlook API, the server retrieves the latest business task information from the user's mailbox and saves the information for each task (task name, scheduled execution date, person in charge, etc.) in a database.

[0963] Step 2:

[0964] Data storage

[0965] The server stores the collected business tasks in a PostgreSQL database.

[0966] Input: Collected work task data

[0967] Output: Business tasks stored in a database

[0968] Specific operation: The server inserts the collected business task information into the Task table and assigns an ID so that each task can be uniquely identified. For example, "Meeting with Client A" is saved as "task_id=101".

[0969] Step 3:

[0970] Task organization

[0971] The server organizes tasks using generative AI (e.g., OpenAI's GPT-3) based on the business tasks stored in the database.

[0972] Input: Business task data stored in the database

[0973] Output: Organized task information

[0974] Specific operation: The server uses a generation AI to extract subtasks for the business task "Preparing monthly reports" - "Data collection," "Report preparation," and "Review." The importance and priority of the generated subtasks are then set.

[0975] Step 4:

[0976] Manual Generation

[0977] The server generates a detailed work manual based on the organized tasks, specifically describing the procedures and important points for each task.

[0978] Input: Organized task information

[0979] Output: Detailed business manual

[0980] Specific operation: The server uses the generation AI to describe in detail the "procedures for creating monthly reports." For example, the template to be used, the data collection method, review points, etc. are written in Markdown format. The generated business manual is saved in the database as "manual_id=201."

[0981] Step 5:

[0982] Automatic creation of handover documents

[0983] The server automatically creates a handover document based on the generated business manual.

[0984] Input: Detailed business manual

[0985] Output: Handover document

[0986] Specific operation: The server uses the generation AI to extract important points and points to note from each business manual and saves them in Markdown format as "Handover_ClientA_Meeting.md." The handover document contains detailed information about the specific procedures and contact information for each business.

[0987] Step 6:

[0988] Responding to user questions

[0989] The user inputs a question to the server from the terminal, such as "Please tell me the format of the monthly report."

[0990] Input: User question

[0991] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[0992] Output: The generated answer

[0993] Specific operation: The server searches the database for "Monthly report creation procedure.md" and uses a generative AI (e.g., GPT-3) to generate a specific answer regarding "monthly report format." The generated answer is then provided to Tanaka, who provides specific guidance such as "The format of the monthly report is as follows:"

[0994] In this way, efficient business handover can be achieved through the specific actions and data flow at each step.

[0995] (Application example 1)

[0996] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0997] The handover of work tasks when introducing or maintaining a factory robot is complex and time-consuming. To efficiently take over work tasks, it is important to accurately understand the work of the predecessor and provide the new operator with appropriate manuals and procedures. However, traditional manual handovers are error-prone and take a lot of time and effort.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0999] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate method for handling a new task by referring to past response history, means for managing work tasks by collecting the operation history, work logs, and sensor data of the factory robot, and means for generating a work manual based on the collected robot work content, thereby enabling quick and accurate handover of work for factory robots.

[1000] "Generative AI" refers to generative interactive artificial intelligence, a technology that performs tasks such as organizing work content, generating manuals, and providing automatic responses.

[1001] "Work content" refers to the tasks and work associated with a particular job or process.

[1002] A "handover document" is a document provided by a predecessor when handing over work to a successor, and it contains important details of the work, contact information, and how to respond.

[1003] "Response methods" refer to the methods or procedures that should be followed for a specific job or task.

[1004] A "factory robot" is a robot used to automate tasks and operations within a factory.

[1005] "Operation history" refers to historical information about the operation of robots and equipment.

[1006] A "work log" is a log that records the details of the work performed by a robot or device.

[1007] "Sensor data" refers to data acquired by a robot or device from built-in or external sensors.

[1008] A "work manual" is a detailed set of instructions for a specific work task.

[1009] A "server" is a computer system for storing, managing, and processing data.

[1010] This invention provides a system that uses generative AI to efficiently transfer tasks between factory robots. The system is mainly composed of a server, a robot, and a user.

[1011] System configuration and operation

[1012] 1. Collecting business details

[1013] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[1014] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[1015] 2. Organizing work content and creating manuals

[1016] The server uses generative AI to organize the work content based on the collected business tasks.

[1017] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[1018] 3. Automatic creation of handover documents

[1019] The server automatically creates a handover document based on the generated business manual.

[1020] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[1021] 4. Responding to user questions

[1022] If the user has any questions about a new business, he or she asks the server a question.

[1023] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[1024] The generated answer is provided to the user, and a response method is suggested.

[1025] This system can be implemented using a programming language such as Python. The server uses a database management system (e.g., MySQL) and a generative AI model (e.g., OpenAI's GPT-3) to process each task. For example, for a factory robot's task of "assembling part A," the generated work manual would contain detailed instructions for "assembling part A."

[1026] Example prompt sentence:

[1027] Task: Create a detailed task manual based on Assemble part A.

[1028] This enables factory robots to take over tasks quickly and accurately.

[1029] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1030] Step 1:

[1031] Collection of business details

[1032] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[1033] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[1034] Input: Instructions for collecting work content from the user

[1035] Output: Collected operation history, work logs, and sensor data are saved in a database

[1036] Step 2:

[1037] Organizing business operations

[1038] The server uses generative AI to organize the work content based on the collected business tasks.

[1039] Input: Business tasks stored in the database

[1040] Output: Tasks organized by the generative AI model

[1041] Specific operation: The server inputs the collected task data into a generative AI model (e.g., OpenAI GPT-3) and requests it to organize the work content. The generative AI analyzes the input data and organizes each task logically.

[1042] Step 3:

[1043] Generate business manuals

[1044] Based on the organized business content, the server uses generation AI to generate a detailed business manual.

[1045] Input: Tasks organized by the generative AI model

[1046] Output: A detailed business manual is generated.

[1047] Specific operation: The server inputs a prompt sentence into the AI ​​model based on the organized work content to generate a specific work manual. For example, the prompt sentence "Please generate a detailed work manual based on the work content: Assemble part A." is used.

[1048] Step 4:

[1049] Automatic creation of handover documents

[1050] The server automatically creates a handover document based on the generated business manual.

[1051] Input: Generated operations manual

[1052] Output: Generated handover document

[1053] Specific operation: Based on the contents of the generated business manual, the server automatically compiles a handover document that includes important business details, response methods, contact information, etc.

[1054] Step 5:

[1055] Responding to user questions

[1056] If the user has any questions about a new business, he or she asks the server a question.

[1057] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[1058] Input: User question

[1059] Output: The correct answer generated

[1060] Specific operation: The server searches the database for relevant business manuals and handover documents based on the question, and inputs the question as a prompt into the generative AI model. The generative AI then generates an appropriate answer to the question and provides it to the user.

[1061] These steps ensure fast and accurate task handover for factory robots.

[1062] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1063] This invention provides a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, as well as a system that combines an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[1064] System configuration and operation

[1065] 1. Collecting business details

[1066] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[1067] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[1068] 2. Organizing work content and creating manuals

[1069] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[1070] 3. Automatic creation of handover documents

[1071] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[1072] 4. Emotion Recognition by Emotion Engine

[1073] The server uses an emotion engine to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[1074] The emotional states recognized by the emotion engine are recorded and stored in a database within the server.

[1075] 5. Adjusting work suggestions based on emotional state

[1076] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1077] 6. Responding to user questions

[1078] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[1079] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[1080] 7. Enhanced recommendations with an emotion engine

[1081] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[1082] Specific operation example

[1083] 1. Collecting business details

[1084] The user (Sato) requests the server to collect business tasks.

[1085] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[1086] 2. Organizing work content and creating manuals

[1087] The server uses a generation AI based on the collected tasks to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system." The generated business manuals are stored on the server.

[1088] 3. Automatic creation of handover documents

[1089] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[1090] 4. Emotion recognition and recording using the emotion engine

[1091] The server recognizes the emotions of the user who is taking over the task in real time. For example, if the user (Tanaka) is feeling stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[1092] 5. Adjusting work suggestions based on emotional state

[1093] The server recognizes that Tanaka is under stress and suggests only presenting high-priority tasks or postponing some tasks.

[1094] 6. Responding to user questions

[1095] A user (Tanaka) asks, "Please tell me the format of the monthly report."

[1096] The server references a database of past work manuals and handover documents and uses a generative AI to generate an appropriate answer regarding the "monthly report format." The answer is presented to Tanaka, who provides specific formatting guidelines.

[1097] 7. Enhanced recommendations with an emotion engine

[1098] The server considers the user's (Tanaka's) fatigue level and proposes measures to reduce the workload, such as suggesting that Tanaka take a short break, in order to improve work efficiency.

[1099] In this way, the present invention provides a system that uses generative AI and an emotion engine to smoothly carry out work handovers during personnel transfers by organizing work, generating manuals, creating handover documents, and responding to questions. By incorporating the user's emotional state, it becomes possible to make more human-friendly work proposals, with the aim of further improving the stability and efficiency of work.

[1100] The processing flow will be explained below.

[1101] Step 1:

[1102] The user requests the server to collect work tasks. Specifically, the user sends an instruction from the terminal to the server saying, "I want work tasks collected."

[1103] Step 2:

[1104] The server collects work tasks from the user's mailbox and task management tool. For example, the server automatically obtains task information such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system," and stores it in a database on the server.

[1105] Step 3:

[1106] The server uses a generation AI to organize the work content based on the collected task information. The server then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," "How to update the CRM system," etc.

[1107] Step 4:

[1108] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[1109] Step 5:

[1110] The server uses an emotion engine to recognize the emotions of the user in real time while the server takes over the task. The server analyzes the user's emotional state (e.g., stress, excitement, fatigue) based on the text or voice information the user inputs through the terminal.

[1111] Step 6:

[1112] The emotional state of the user recognized by the emotion engine is recorded and stored in a database in the server.

[1113] Step 7:

[1114] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1115] Step 8:

[1116] The user sends a question to the server about a task that he or she is unsure about. Specifically, the user sends a question from the terminal, such as "Please tell me the format of the monthly report."

[1117] Step 9:

[1118] The server references a database of past work manuals and handover documents and uses AI to generate appropriate answers.The server prepares specific answers regarding the "monthly report format" based on past response history and stores them on the server.

[1119] Step 10:

[1120] The server provides the generated answer to the user, for example, notifying the user of the format of the monthly report and providing specific guidance.

[1121] Step 11:

[1122] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, the server will suggest ways to reduce the workload. Specifically, it will suggest the user take a short break to improve work efficiency.

[1123] Example 2

[1124] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1125] In conventional task handover systems, the handover from predecessor to successor is done manually, which is time-consuming and labor-intensive, and the content of the handover is often subjective and inconsistent. Furthermore, because the system does not take the user's emotional state into consideration, there is a problem with task proposals that do not fully address the user's stress or fatigue. As a result, tasks can lack efficiency and accuracy. To solve these issues, a system is needed that can efficiently organize task content and create handover documents, as well as flexibly propose tasks based on the user's emotional state.

[1126] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1127] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing appropriate ways of handling new work by referring to past response history, means for including an emotion engine for analyzing the user's emotions and adjusting the proposed work content based on the user's emotional state, and means for generating appropriate answers to user questions by referring to a past database using a generation AI. This makes it possible to efficiently and accurately organize work content, create a handover document, and make flexible work proposals and respond to questions while taking the user's emotional state into consideration.

[1128] "Generative AI" is a system that automatically generates text and data using artificial intelligence technology.

[1129] "Work organization" is the process of systematically analyzing a collection of complex work tasks and converting them into an easily understandable format.

[1130] A "handover document" is a document that summarizes the job content, procedures, important matters, etc. from the predecessor to the successor.

[1131] "Past response history" refers to records of previously performed tasks and responses.

[1132] An "appropriate response method" is the most effective and efficient way to respond to a task or problem.

[1133] An "emotion engine" is a system for analyzing and identifying a user's emotional state.

[1134] An "emotional state" is the mental and emotional state a user feels in response to a particular situation or environment.

[1135] "Adjusting task proposals" refers to changing the priority and order of tasks based on the user's emotional state, etc.

[1136] "Answers to questions" are solutions or information provided to users' questions or problems.

[1137] A "database" is a system for systematically storing, managing, and searching specific data.

[1138] A "server" is a computer system that provides data and services over a network.

[1139] "User" means the end user of this system.

[1140] "Means" are methods, tools, techniques, etc. used to achieve a particular goal or objective.

[1141] This invention is a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, and also combines it with an emotion engine that recognizes user emotions. This system is mainly composed of a server, a terminal, and a user.

[1142] System configuration and operation

[1143] Collection of business details

[1144] The user requests the server to collect work tasks. The server collects the work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello) and stores them in a database. For example, it extracts and stores task information such as "meeting with a client," "creating a monthly report," and "updating the CRM system."

[1145] Organizing work content and creating manuals

[1146] The server inputs the collected task information into a generation AI (e.g., OpenAI GPT-3) to generate a specific business manual. For example, the generation AI receives the following prompt:

[1147] "Create a detailed operations manual based on the collected task information."

[1148] This generates documents such as "Procedures for preparing for meetings with clients," "Procedures for creating monthly reports," and "How to update the CRM system." The generated business manuals are stored on the server.

[1149] Automatic creation of handover documents

[1150] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, execution methods, important points to note, contact information, etc. The generated handover document is saved on the server and is ready to be provided to the successor.

[1151] Emotion recognition by emotion engine

[1152] The server analyzes the text and voice information entered by the user using an emotion engine (e.g., IBM Watson Emotion Analysis). For example, the server recognizes the user's emotional state, such as stress or fatigue, during the handover process and records it in a database within the server. The server also analyzes the text and voice information using the following prompt:

[1153] "Analyze and record the user's emotional state based on text and voice input."

[1154] Adjusting work suggestions based on emotional state

[1155] The server adjusts its task suggestions based on the user's recorded emotional state: for example, if the user is stressed, the server may present only high-priority tasks or suggest postponing some tasks.

[1156] Responding to user questions

[1157] The user (successor) asks the server a question about work. For example, "Please tell me the format of the monthly report." The server generates a specific answer by referring to the generation AI and past databases and presents it to the user. The specific prompt is as follows:

[1158] "Can you give me an example of a monthly report and the format?"

[1159] Enhanced recommendations with an emotion engine

[1160] The server then analyzes the user's emotional state again and strengthens its work suggestions. For example, if the user is fatigued, the emotion engine will instruct the server to "suggest a short break" to improve work efficiency.

[1161] Specific operation example

[1162] 1. The user requests the server to collect business tasks.

[1163] 2. The server collects task information from mailboxes and task management tools and stores it in a database.

[1164] 3. The server uses the generation AI to generate the business manual and stores it on the server.

[1165] 4. The server creates a detailed handover document based on the generated business manual and stores it on the server.

[1166] 5. The server analyzes the information entered by the user using an emotion engine and records the recognized emotional state in a database.

[1167] 6. The server adjusts and presents task suggestions to the user based on the user's emotional state.

[1168] 7. The user asks a question, and the server uses generative AI to generate an answer and presents it to the user.

[1169] 8. The server proposes measures to reduce the workload based on the user's emotional state, thereby improving work efficiency.

[1170] This provides a system that enables highly efficient task handover through task organization, manual generation, handover document creation, and question response. By incorporating the user's emotional state, it is possible to make more human-friendly task proposals, aiming to further improve the stability and efficiency of tasks.

[1171] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1172] Step 1:

[1173] The user requests the collection of work tasks. The user inputs the command "Collect work tasks" to the server. The server receives this input and begins collecting work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello). The collected task information (e.g., "Meeting with client," "Preparing monthly report," "Updating CRM system," etc.) is extracted and stored in a database on the server.

[1174] Step 2:

[1175] The server inputs data into the generative AI based on the task information collected in step 1. Specific task information is used as input. A generative AI model (e.g., OpenAI GPT-3) is used to generate detailed business manuals for each task. For example, the generative AI receives a prompt such as, "Please create a detailed business manual based on the collected task information." The output generated in response to this prompt is a business manual such as "Procedures for preparing for a meeting with a client," "Procedures for creating a monthly report," or "How to update the CRM system," which is stored on the server.

[1176] Step 3:

[1177] The server creates a handover document based on the business manual generated in step 2. First, it uses the business manual as input and automatically extracts and organizes important business details, execution methods, important points to note, contact information, etc. As a result of this data processing, a "handover document" is generated. The handover document contains detailed business details and is saved on the server.

[1178] Step 4:

[1179] The server sends the text and voice information entered by the user to an emotion engine (e.g., IBM Watson Emotion Analysis). The user's text and voice information are used as input, and the emotion engine analyzes them. The output of the emotion engine is the user's emotional state (e.g., stress, excitement, fatigue, etc.) and is recorded in a database on the server.

[1180] Step 5:

[1181] The server adjusts the task suggestions based on the user's emotional state recorded in step 4. Using the emotional state data as input, the server prioritizes the tasks based on this data. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks. The adjusted results are then presented to the user.

[1182] Step 6:

[1183] The user sends a business-related question to the server. For example, the user might input a question like, "What is the format of a monthly report?" The server receives this input and inputs the question into the generation AI. The prompt text is, "Please tell me an example of a monthly report and its format." The generation AI generates an appropriate answer, and this output is returned to the server. Finally, the appropriate answer is provided to the user.

[1184] Step 7:

[1185] The server then re-analyzes the user's emotional state and enhances its work suggestions. The emotional state data is used as input, and the server makes optimal work suggestions to the user based on the results of the emotion engine. For example, if the user is fatigued, the emotion engine outputs a command to the server saying, "Please suggest a short break." The server then provides this suggestion to the user, aiming to improve work efficiency.

[1186] Through these processing steps, the system can efficiently and accurately carry out task handover. Furthermore, by taking the user's emotional state into consideration, it is possible to propose tasks that are more human-friendly, thereby improving the stability and efficiency of tasks.

[1187] (Application example 2)

[1188] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1189] In systems aimed at improving work handover and work efficiency, suggestions that do not take into account the emotional state of employees can increase employee stress and fatigue. Furthermore, when employees ask questions about unclear work content, they may not receive appropriate answers quickly. To address these issues, a system that recognizes employees' emotions in real time and generates work suggestions and answers is needed.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1191] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate way to handle new work by referring to past response history, means for recognizing employee emotions in real time and adjusting work proposals based on this, means for generating appropriate answers when employees ask questions about work, and means for proposing measures to reduce workload and prioritization based on emotions. This enables work handover that takes into account the emotional state of employees and improves work efficiency.

[1192] "Generative AI" is artificial intelligence that automatically generates content based on collected data.

[1193] "Organizing work content" is the process of classifying collected work tasks and compiling detailed procedures and points to note.

[1194] "Automatic handover document creation" is a function that automatically creates a document detailing the work to be handed over from the predecessor to the successor.

[1195] "A means of proposing appropriate ways to respond to new tasks by referring to past response history" is a function that proposes the optimal response to new tasks based on data from past tasks.

[1196] "Emotion recognition" is the process of analyzing an employee's text or voice data to understand their emotional state.

[1197] "Adjustment of work proposals" is a function that changes the content and order of work presented based on the employee's emotional state.

[1198] "Generating answers to questions" is the process of providing the most appropriate answer based on past data when an employee asks a question about something they don't understand at work.

[1199] "Workload reduction measures" are methods for reducing workload by adjusting the amount and importance of work, taking into account the emotional state of employees.

[1200] This invention is a system that uses generative AI to automatically organize the work content in a logistics center and combines it with an emotion engine that recognizes the emotions of employees. This system is mainly composed of a server, terminals, and users.

[1201] System configuration and operation

[1202] 1. Collecting business details

[1203] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[1204] The server collects business tasks from users' mailboxes and task management tools. This includes task information such as inventory checks, shipping, etc. The collected task information is stored in a database on the server.

[1205] 2. Organizing work content and creating manuals

[1206] The server uses a generative AI model (such as TextGenie) to organize the work content based on the collected task information. It then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "procedures for inventory entry and retrieval work," "procedures for inventory confirmation," and "procedures for shipping work."

[1207] 3. Automatic creation of handover documents

[1208] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc., and the generated handover document is saved on the server.

[1209] 4. Emotion Recognition by Emotion Engine

[1210] The server uses an emotion engine (e.g., EmotionEngine) to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[1211] 5. Adjusting work suggestions based on emotional state

[1212] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1213] 6. Responding to user questions

[1214] The user asks the server a question about a task they are unsure about. The server then references a database of past task manuals and handover documents and uses a generative AI model to generate an appropriate answer. This allows the user to quickly obtain a specific answer.

[1215] 7. Enhanced recommendations with an emotion engine

[1216] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[1217] Specific operation example

[1218] Collection of business details

[1219] The user requests the server to collect business tasks.

[1220] The server collects tasks such as "warehouse entry and exit operations," "inventory checks," and "shipping operations" from users' mailboxes and task management tools and stores them in a database.

[1221] Organizing work content and creating manuals

[1222] The server uses AI generation to generate detailed business manuals based on the collected tasks, such as "procedures for receiving and retrieving goods," "procedures for inventory checks," and "procedures for shipping work." The generated business manuals are stored on the server.

[1223] Automatic creation of handover documents

[1224] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[1225] Emotion recognition and recording by emotion engine

[1226] The server recognizes the emotions of the user in real time during the handover process. For example, if the user feels stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[1227] Adjusting work suggestions based on emotional state

[1228] The server recognizes that the user is under stress and presents only high-priority tasks or suggests postponing some tasks.

[1229] Responding to user questions

[1230] The user asks, "Please tell me the procedure for loading and unloading."

[1231] The server references a database of past work manuals and handover documents, and uses a generative AI to generate an appropriate answer regarding "procedures for warehousing and shipping work." The answer is presented to the user, providing specific procedural guidelines.

[1232] Enhanced recommendations with an emotion engine

[1233] The server considers the user's level of fatigue and suggests ways to reduce the workload, such as suggesting that the user take a short break, in order to improve work efficiency.

[1234] Prompt Sentence Examples

[1235] By inputting the following prompt sentence into the generative AI model, the necessary business manual will be generated.

[1236] Please generate an operational manual for a logistics center. The collected tasks are as follows:

[1237] 1. Incoming and outgoing work

[1238] 2. Check inventory

[1239] 3. Shipping

[1240] Include detailed steps and notes for each task.

[1241] In this way, by using the system of the present invention, it is possible to significantly improve the efficiency of work within a logistics center. In addition, by making suggestions that take into account the emotional state of employees, it is possible to reduce the burden on employees and provide a comfortable working environment.

[1242] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1243] Step 1:

[1244] The user requests the server to collect business tasks. The user sends an instruction to the server via their terminal to "collect business tasks." Upon receiving this input, the server accesses the configured mailbox and task management tool to collect data on business tasks (e.g., warehousing and shipping work, inventory checks, shipping work). As an output, the collected task information is saved in the server's database.

[1245] Step 2:

[1246] The server organizes business tasks and generates a business manual that includes detailed procedures and important points. Based on the collected task information, the server uses a generative AI model (e.g., textgenie) to classify business tasks and generate specific procedures for each task. For example, it might include "detailed procedures for inventory entry and retrieval work" and "important points for inventory checks." Based on this input process, the organized business content is output as a text-format business manual and saved on the server.

[1247] Step 3:

[1248] The server automatically creates handover documents based on the generated business manuals. The server extracts important points from each business manual and automatically generates a series of handover documents. The handover documents include the job content, procedures, response methods, contact information, etc., and are recorded in the server's database. The generated handover documents are then ready to be provided to the new employee.

[1249] Step 4:

[1250] The server recognizes the user's emotional state in real time. The user inputs emotional data via text or voice through the device. The server analyzes this input data using an emotion engine (e.g., EmotionEngine) to determine the user's emotional state (e.g., stress, excitement, fatigue). This emotional state data is stored on the server.

[1251] Step 5:

[1252] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server will suggest only high-priority tasks or postpone some tasks. Based on this adjustment suggestion, a task priority list is generated and sent to the user's device.

[1253] Step 6:

[1254] The user requests a question about work from the server. The user sends a specific question (e.g., "Please tell me the procedure for warehouse entry and exit work") to the server via their device. The server receives this input data, references a database of past work manuals and handover documents, and uses a generative AI model to generate the optimal answer. The generated answer is sent to the user's device in text format.

[1255] Step 7:

[1256] The server takes the user's emotional state into account to make more appropriate work suggestions. The server reanalyzes the emotional data and, if the user indicates fatigue, generates work load reduction measures, such as suggesting a short break. These specific suggestions are displayed in text format on the user's device.

[1257] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1258] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1259] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1260] [Fourth embodiment]

[1261] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1262] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1263] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1264] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1265] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1266] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1267] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1268] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1269] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1270] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1271] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1272] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1273] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1274] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[1275] System configuration and operation

[1276] 1. Collecting business details

[1277] The user instructs the server to collect related business details.

[1278] The server collects work tasks from users' emails, task management tools, etc. For example, tasks such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system" are collected.

[1279] The collected business tasks are stored in a database on the server.

[1280] 2. Organizing work content and creating manuals

[1281] The server uses generative AI to organize the work content based on the collected business tasks.

[1282] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[1283] 3. Automatic creation of handover documents

[1284] The server automatically creates a handover document based on the generated business manual.

[1285] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[1286] 4. Responding to user questions

[1287] If the user has any questions about a new business, he or she asks the server a question.

[1288] The server references a database of previously created business manuals and handover documents and uses AI to generate appropriate answers.

[1289] The generated answer is provided to the user, and a response method is suggested.

[1290] Specific operation example

[1291] 1. Collecting business details

[1292] The user (Sato) requests the server to collect business tasks.

[1293] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[1294] 2. Organizing work content and creating manuals

[1295] Based on the collected tasks, the server uses a generation AI to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system."

[1296] The generated business manual is stored on the server.

[1297] 3. Automatic creation of handover documents

[1298] The server uses the generated business manual to automatically create a detailed handover document.

[1299] The handover document contains the necessary work content, how to carry it out, important points to note, contact information, etc., and is saved on the server. This handover document is provided when the work is handed over to a successor (for example, Tanaka).

[1300] 4. Responding to user questions

[1301] The user (Tanaka) asks the server, "Please tell me the format of the monthly report."

[1302] The server refers to a database of past business manuals and handover documents and uses a generation AI to generate an appropriate answer regarding the "monthly report format."

[1303] The generated answers are provided to Tanaka to guide him in creating an appropriate report.

[1304] In this way, the present invention provides a system that allows for smooth work handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[1305] The processing flow will be explained below.

[1306] Step 1:

[1307] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[1308] Step 2:

[1309] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[1310] Step 3:

[1311] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[1312] Step 4:

[1313] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[1314] Step 5:

[1315] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[1316] Step 6:

[1317] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[1318] Step 7:

[1319] The server provides the generated answer to the user, for example, by informing the user, "Here is the format for the monthly report," and providing specific formatting guidelines.

[1320] Example 1

[1321] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1322] In the conventional business handover process, the predecessor must manually organize the business content and create manuals and handover documents, which is time-consuming and labor-intensive, and there is a high possibility of gaps or inconsistencies in business knowledge. Furthermore, even after the business handover, it is difficult for the new employee to easily refer to past response history and learn appropriate response methods.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1324] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate response method for a new work task by referring to past response history, means for collecting work tasks based on user instructions, means for saving the collected work tasks in a database, means for generating a work manual using a generation AI based on the work tasks saved in the database, means for automatically creating a handover document based on the generated work manual, and means for generating appropriate answers to user questions. This streamlines the work handover process, reduces knowledge gaps and inconsistencies from the predecessor to the successor, and enables the successor to easily refer to past response history to obtain an appropriate response method.

[1325] "Generative AI" refers to artificial intelligence models that automatically create natural language text and other outputs generated based on user input and data.

[1326] "Means for automatically organizing work content" refers to the method or process by which the system processes the work tasks and data collected by the server and automatically organizes and classifies them using generative AI.

[1327] "Means for automatically creating handover documents" refers to a method or process in which a server uses generative AI or other algorithms to automatically organize work content and procedures and create handover documents.

[1328] "Means of proposing appropriate response methods for new tasks by referring to past response history" refers to a method or process for proposing appropriate response methods for new tasks by using generative AI or search algorithms based on data and manuals that the server has collected and stored in the past.

[1329] A "means for collecting business tasks based on user instructions" is a method or process by which a user instructs a server to collect business tasks through the system, and the server collects the necessary information from the user's relevant data sources based on that instruction.

[1330] "Means for storing collected business tasks in a database" refers to a method or process by which the server stores collected business tasks and data in a database and makes them available for later processing or reference.

[1331] "Means for generating business manuals using generative AI based on business tasks stored in a database" refers to a method or process by which a server reads business task data stored in a database and automatically creates detailed business manuals using generative AI.

[1332] "Means for automatically creating a handover document based on a generated business manual" refers to a method or process by which a server automatically creates a handover document by referring to the business manual created by the generation AI.

[1333] "Means for generating appropriate answers to user questions" refers to a method or process by which a server receives a question from a user, automatically generates an appropriate answer using a generation AI, and provides it to the user.

[1334] This invention provides a system that uses a generation AI to automatically organize work content and efficiently transfer work from predecessor to successor. This system is mainly composed of a server, terminals, and users.

[1335] System configuration and operation overview

[1336] This system consists of a server, terminals, and users, and uses the following specific hardware and software:

[1337] Server: Processes and stores data (e.g., cloud server, on-premise server).

[1338] Terminal: A device used by a user to perform operations (e.g., PC, smartphone).

[1339] Database: A database management system such as PostgreSQL.

[1340] Generative AI models: Natural language generation models such as OpenAI's GPT-3.

[1341] API: An interface for connecting user data sources (email, task management tools) with the server (e.g., Microsoft Outlook API, Trello API).

[1342] Details of data processing and calculation

[1343] 1. Collecting business details

[1344] The user instructs the server to collect business tasks from the terminal, for example, by clicking the "Start collecting business tasks" button on the web dashboard.

[1345] The server collects business tasks from the user's mailbox and task management tool. Specifically, it uses the Microsoft Outlook API to obtain task information such as "Meeting with Client A" and "Preparing monthly report" from the mailbox.

[1346] The server stores the collected business tasks in a PostgreSQL database, along with information about each task (such as the task name, scheduled execution date, and person in charge).

[1347] 2. Organizing work content and creating manuals

[1348] The server uses generative AI (OpenAI's GPT-3) to organize tasks based on the business tasks stored in the database. For example, for the task "Preparing a monthly report," it extracts the subtasks "Data collection," "Report preparation," and "Review."

[1349] The server generates detailed work manuals based on the organized tasks. It specifically describes the procedures and important points for each task. For example, the "procedure for creating monthly reports" section includes the format template to be used and the specific method for collecting data.

[1350] The server saves the generated business manual in a database in Markdown format. For example, it saves it as "Monthly report creation procedure.md".

[1351] 3. Automatic creation of handover documents

[1352] The server automatically generates a handover document based on the generated business manual. The handover document includes information such as the job content, procedures, important points, and contact information. For example, it generates a section called "Important points to convey to the next successor."

[1353] The server saves the generated handover document in the database in Markdown format. For example, it saves it as "Handover_ClientA_Meeting.md".

[1354] 4. Responding to user questions

[1355] The user inputs a question to the server from the terminal, for example, "What is the format of the monthly report?"

[1356] The server searches the database for relevant business manuals and handover documents and generates appropriate answers using generation AI. For example, it extracts format information from "Monthly Report Creation Procedure.md."

[1357] The server provides the generated answer to the user, for example, "The format for the monthly report is as follows:"

[1358] Specific operation example

[1359] 1. The user (Sato) clicks the "Start collecting business tasks" button on the web dashboard.

[1360] 2. The server uses the Microsoft Outlook API to collect tasks such as "Meeting with Client A" from Sato's mailbox and save them in a database.

[1361] 3. The server uses GPT-3 to break down the task of "preparing monthly reports" into the subtasks of "data collection," "report preparation," and "review," and generates a manual.

[1362] 4. The server saves the manual as "Monthly Report Creation Procedure.md" and also generates a handover document.

[1363] 5. When a user (Tanaka) asks, "Please tell me the format of the monthly report," the server uses GPT-3 to generate an answer and provides it to Tanaka.

[1364] Prompt Sentence Examples

[1365] When a user asks a question to the server, the prompt is:

[1366] "Please tell me the format of the monthly report."

[1367] In this way, the present invention provides a system that allows for smooth business handover during personnel transfers by using generation AI to organize work, generate manuals, create handover documents, and respond to questions.

[1368] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1369] Step 1:

[1370] User Instructions and Data Collection

[1371] Users access the system from a terminal (e.g., PC or smartphone) and instruct the server to collect business tasks. Specifically, collection begins by clicking the "Start business task collection" button on the web dashboard.

[1372] Input: User's instruction to start collection

[1373] The server receives instructions from the user and collects work tasks from the user's mailbox (e.g., using the Microsoft Outlook API) or task management tool (e.g., using the Trello API). For example, it retrieves schedules such as "Meeting with Client A" via the Microsoft Outlook API.

[1374] Output: Collected work task data

[1375] Specific operation: Using the Microsoft Outlook API, the server retrieves the latest business task information from the user's mailbox and saves the information for each task (task name, scheduled execution date, person in charge, etc.) in a database.

[1376] Step 2:

[1377] Data storage

[1378] The server stores the collected business tasks in a PostgreSQL database.

[1379] Input: Collected work task data

[1380] Output: Business tasks stored in a database

[1381] Specific operation: The server inserts the collected business task information into the Task table and assigns an ID so that each task can be uniquely identified. For example, "Meeting with Client A" is saved as "task_id=101".

[1382] Step 3:

[1383] Task organization

[1384] The server organizes tasks using generative AI (e.g., OpenAI's GPT-3) based on the business tasks stored in the database.

[1385] Input: Business task data stored in the database

[1386] Output: Organized task information

[1387] Specific operation: The server uses a generation AI to extract subtasks for the business task "Preparing monthly reports" - "Data collection," "Report preparation," and "Review." The importance and priority of the generated subtasks are then set.

[1388] Step 4:

[1389] Manual Generation

[1390] The server generates a detailed work manual based on the organized tasks, specifically describing the procedures and important points for each task.

[1391] Input: Organized task information

[1392] Output: Detailed business manual

[1393] Specific operation: The server uses the generation AI to describe in detail the "procedures for creating monthly reports." For example, the template to be used, the data collection method, review points, etc. are written in Markdown format. The generated business manual is saved in the database as "manual_id=201."

[1394] Step 5:

[1395] Automatic creation of handover documents

[1396] The server automatically creates a handover document based on the generated business manual.

[1397] Input: Detailed business manual

[1398] Output: Handover document

[1399] Specific operation: The server uses the generation AI to extract important points and points to note from each business manual and saves them in Markdown format as "Handover_ClientA_Meeting.md." The handover document contains detailed information about the specific procedures and contact information for each business.

[1400] Step 6:

[1401] Responding to user questions

[1402] The user inputs a question to the server from the terminal, such as "Please tell me the format of the monthly report."

[1403] Input: User question

[1404] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[1405] Output: The generated answer

[1406] Specific operation: The server searches the database for "Monthly report creation procedure.md" and uses a generative AI (e.g., GPT-3) to generate a specific answer regarding "monthly report format." The generated answer is then provided to Tanaka, who provides specific guidance such as "The format of the monthly report is as follows:"

[1407] In this way, efficient business handover can be achieved through the specific actions and data flow at each step.

[1408] (Application example 1)

[1409] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1410] The handover of work tasks when introducing or maintaining a factory robot is complex and time-consuming. To efficiently take over work tasks, it is important to accurately understand the work of the predecessor and provide the new operator with appropriate manuals and procedures. However, traditional manual handovers are error-prone and take a lot of time and effort.

[1411] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1412] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate method for handling a new task by referring to past response history, means for managing work tasks by collecting the operation history, work logs, and sensor data of the factory robot, and means for generating a work manual based on the collected robot work content, thereby enabling quick and accurate handover of work for factory robots.

[1413] "Generative AI" refers to generative interactive artificial intelligence, a technology that performs tasks such as organizing work content, generating manuals, and providing automatic responses.

[1414] "Work content" refers to the tasks and work associated with a particular job or process.

[1415] A "handover document" is a document provided by a predecessor when handing over work to a successor, and it contains important details of the work, contact information, and how to respond.

[1416] "Response methods" refer to the methods or procedures that should be followed for a specific job or task.

[1417] A "factory robot" is a robot used to automate tasks and operations within a factory.

[1418] "Operation history" refers to historical information about the operation of robots and equipment.

[1419] A "work log" is a log that records the details of the work performed by a robot or device.

[1420] "Sensor data" refers to data acquired by a robot or device from built-in or external sensors.

[1421] A "work manual" is a detailed set of instructions for a specific work task.

[1422] A "server" is a computer system for storing, managing, and processing data.

[1423] This invention provides a system that uses generative AI to efficiently transfer tasks between factory robots. The system is mainly composed of a server, a robot, and a user.

[1424] System configuration and operation

[1425] 1. Collecting business details

[1426] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[1427] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[1428] 2. Organizing work content and creating manuals

[1429] The server uses generative AI to organize the work content based on the collected business tasks.

[1430] A detailed business manual is generated for the organized business content. The generated manual includes the execution procedures and important points for each task.

[1431] 3. Automatic creation of handover documents

[1432] The server automatically creates a handover document based on the generated business manual.

[1433] The handover document contains important details of the work, how to respond, contact information, etc. This handover document is saved on the server and provided to the successor.

[1434] 4. Responding to user questions

[1435] If the user has any questions about a new business, he or she asks the server a question.

[1436] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[1437] The generated answer is provided to the user, and a response method is suggested.

[1438] This system can be implemented using a programming language such as Python. The server uses a database management system (e.g., MySQL) and a generative AI model (e.g., OpenAI's GPT-3) to process each task. For example, for a factory robot's task of "assembling part A," the generated work manual would contain detailed instructions for "assembling part A."

[1439] Example prompt sentence:

[1440] Task: Create a detailed task manual based on Assemble part A.

[1441] This enables factory robots to take over tasks quickly and accurately.

[1442] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1443] Step 1:

[1444] Collection of business details

[1445] The user instructs the server to collect information about the factory robot's operations, such as its operating history, work logs, and sensor data.

[1446] The server collects the robot's operation history, work logs, and sensor data, and stores them in a database. For example, tasks such as "assembly of part A" and "quality inspection of product B" are collected.

[1447] Input: Instructions for collecting work content from the user

[1448] Output: Collected operation history, work logs, and sensor data are saved in a database

[1449] Step 2:

[1450] Organizing business operations

[1451] The server uses generative AI to organize the work content based on the collected business tasks.

[1452] Input: Business tasks stored in the database

[1453] Output: Tasks organized by the generative AI model

[1454] Specific operation: The server inputs the collected task data into a generative AI model (e.g., OpenAI GPT-3) and requests it to organize the work content. The generative AI analyzes the input data and organizes each task logically.

[1455] Step 3:

[1456] Generate business manuals

[1457] Based on the organized business content, the server uses generation AI to generate a detailed business manual.

[1458] Input: Tasks organized by the generative AI model

[1459] Output: A detailed business manual is generated.

[1460] Specific operation: The server inputs a prompt sentence into the AI ​​model based on the organized work content to generate a specific work manual. For example, the prompt sentence "Please generate a detailed work manual based on the work content: Assemble part A." is used.

[1461] Step 4:

[1462] Automatic creation of handover documents

[1463] The server automatically creates a handover document based on the generated business manual.

[1464] Input: Generated operations manual

[1465] Output: Generated handover document

[1466] Specific operation: Based on the contents of the generated business manual, the server automatically compiles a handover document that includes important business details, response methods, contact information, etc.

[1467] Step 5:

[1468] Responding to user questions

[1469] If the user has any questions about a new business, he or she asks the server a question.

[1470] The server references a database of previously created business manuals and handover documents and uses a generation AI to generate appropriate answers.

[1471] Input: User question

[1472] Output: The correct answer generated

[1473] Specific operation: The server searches the database for relevant business manuals and handover documents based on the question, and inputs the question as a prompt into the generative AI model. The generative AI then generates an appropriate answer to the question and provides it to the user.

[1474] These steps ensure fast and accurate task handover for factory robots.

[1475] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1476] This invention provides a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, as well as a system that combines an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[1477] System configuration and operation

[1478] 1. Collecting business details

[1479] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[1480] The server collects work tasks from the user's mailbox and task management tool. For example, it obtains task information such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system," and stores the extracted task information in a database on the server.

[1481] 2. Organizing work content and creating manuals

[1482] The server uses generation AI to organize the work content based on the collected task information. It generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," and "How to update the CRM system."

[1483] 3. Automatic creation of handover documents

[1484] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[1485] 4. Emotion Recognition by Emotion Engine

[1486] The server uses an emotion engine to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[1487] The emotional states recognized by the emotion engine are recorded and stored in a database within the server.

[1488] 5. Adjusting work suggestions based on emotional state

[1489] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1490] 6. Responding to user questions

[1491] The user (successor) asks the server for a question about a task that he or she is unsure about. For example, the user may ask, "Please tell me the format of the monthly report."

[1492] The server references a database of past business manuals and handover documents and uses AI to generate appropriate answers. Based on past response history, a specific answer regarding the "monthly report format" is prepared and saved on the server.

[1493] 7. Enhanced recommendations with an emotion engine

[1494] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[1495] Specific operation example

[1496] 1. Collecting business details

[1497] The user (Sato) requests the server to collect business tasks.

[1498] The server collects tasks such as "Meeting with Client A," "Preparing monthly report," and "Updating CRM system" from Sato's mailbox and task management tool and stores them in a database.

[1499] 2. Organizing work content and creating manuals

[1500] The server uses a generation AI based on the collected tasks to generate detailed business manuals such as "preparing for a meeting with Client A," "procedures for creating monthly reports," and "how to update the CRM system." The generated business manuals are stored on the server.

[1501] 3. Automatic creation of handover documents

[1502] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[1503] 4. Emotion recognition and recording using the emotion engine

[1504] The server recognizes the emotions of the user who is taking over the task in real time. For example, if the user (Tanaka) is feeling stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[1505] 5. Adjusting work suggestions based on emotional state

[1506] The server recognizes that Tanaka is under stress and suggests only presenting high-priority tasks or postponing some tasks.

[1507] 6. Responding to user questions

[1508] A user (Tanaka) asks, "Please tell me the format of the monthly report."

[1509] The server references a database of past work manuals and handover documents and uses a generative AI to generate an appropriate answer regarding the "monthly report format." The answer is presented to Tanaka, who provides specific formatting guidelines.

[1510] 7. Enhanced recommendations with an emotion engine

[1511] The server considers the user's (Tanaka's) fatigue level and proposes measures to reduce the workload, such as suggesting that Tanaka take a short break, in order to improve work efficiency.

[1512] In this way, the present invention provides a system that uses generative AI and an emotion engine to smoothly carry out work handovers during personnel transfers by organizing work, generating manuals, creating handover documents, and responding to questions. By incorporating the user's emotional state, it becomes possible to make more human-friendly work proposals, with the aim of further improving the stability and efficiency of work.

[1513] The processing flow will be explained below.

[1514] Step 1:

[1515] The user requests the server to collect work tasks. Specifically, the user sends an instruction from the terminal to the server saying, "I want work tasks collected."

[1516] Step 2:

[1517] The server collects work tasks from the user's mailbox and task management tool. For example, the server automatically obtains task information such as "Meeting with Client A," "Preparing monthly reports," and "Updating the CRM system," and stores it in a database on the server.

[1518] Step 3:

[1519] The server uses a generation AI to organize the work content based on the collected task information. The server then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "Preparing for a meeting with Client A," "Procedures for creating monthly reports," "How to update the CRM system," etc.

[1520] Step 4:

[1521] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc. The generated handover document is saved on the server and prepared for distribution to the successor.

[1522] Step 5:

[1523] The server uses an emotion engine to recognize the emotions of the user in real time while the server takes over the task. The server analyzes the user's emotional state (e.g., stress, excitement, fatigue) based on the text or voice information the user inputs through the terminal.

[1524] Step 6:

[1525] The emotional state of the user recognized by the emotion engine is recorded and stored in a database in the server.

[1526] Step 7:

[1527] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1528] Step 8:

[1529] The user sends a question to the server about a task that he or she is unsure about. Specifically, the user sends a question from the terminal, such as "Please tell me the format of the monthly report."

[1530] Step 9:

[1531] The server references a database of past work manuals and handover documents and uses AI to generate appropriate answers.The server prepares specific answers regarding the "monthly report format" based on past response history and stores them on the server.

[1532] Step 10:

[1533] The server provides the generated answer to the user, for example, notifying the user of the format of the monthly report and providing specific guidance.

[1534] Step 11:

[1535] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, the server will suggest ways to reduce the workload. Specifically, it will suggest the user take a short break to improve work efficiency.

[1536] Example 2

[1537] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1538] In conventional task handover systems, the handover from predecessor to successor is done manually, which is time-consuming and labor-intensive, and the content of the handover is often subjective and inconsistent. Furthermore, because the system does not take the user's emotional state into consideration, there is a problem with task proposals that do not fully address the user's stress or fatigue. As a result, tasks can lack efficiency and accuracy. To solve these issues, a system is needed that can efficiently organize task content and create handover documents, as well as flexibly propose tasks based on the user's emotional state.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1540] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing appropriate ways of handling new work by referring to past response history, means for including an emotion engine for analyzing the user's emotions and adjusting the proposed work content based on the user's emotional state, and means for generating appropriate answers to user questions by referring to a past database using a generation AI. This makes it possible to efficiently and accurately organize work content, create a handover document, and make flexible work proposals and respond to questions while taking the user's emotional state into consideration.

[1541] "Generative AI" is a system that automatically generates text and data using artificial intelligence technology.

[1542] "Work organization" is the process of systematically analyzing a collection of complex work tasks and converting them into an easily understandable format.

[1543] A "handover document" is a document that summarizes the job content, procedures, important matters, etc. from the predecessor to the successor.

[1544] "Past response history" refers to records of previously performed tasks and responses.

[1545] An "appropriate response method" is the most effective and efficient way to respond to a task or problem.

[1546] An "emotion engine" is a system for analyzing and identifying a user's emotional state.

[1547] An "emotional state" is the mental and emotional state a user feels in response to a particular situation or environment.

[1548] "Adjusting task proposals" refers to changing the priority and order of tasks based on the user's emotional state, etc.

[1549] "Answers to questions" are solutions or information provided to users' questions or problems.

[1550] A "database" is a system for systematically storing, managing, and searching specific data.

[1551] A "server" is a computer system that provides data and services over a network.

[1552] "User" means the end user of this system.

[1553] "Means" are methods, tools, techniques, etc. used to achieve a particular goal or objective.

[1554] This invention is a system that uses generative AI to automatically organize work content and efficiently transfer work from predecessor to successor, and also combines it with an emotion engine that recognizes user emotions. This system is mainly composed of a server, a terminal, and a user.

[1555] System configuration and operation

[1556] Collection of business details

[1557] The user requests the server to collect work tasks. The server collects the work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello) and stores them in a database. For example, it extracts and stores task information such as "meeting with a client," "creating a monthly report," and "updating the CRM system."

[1558] Organizing work content and creating manuals

[1559] The server inputs the collected task information into a generation AI (e.g., OpenAI GPT-3) to generate a specific business manual. For example, the generation AI receives the following prompt:

[1560] "Create a detailed operations manual based on the collected task information."

[1561] This generates documents such as "Procedures for preparing for meetings with clients," "Procedures for creating monthly reports," and "How to update the CRM system." The generated business manuals are stored on the server.

[1562] Automatic creation of handover documents

[1563] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, execution methods, important points to note, contact information, etc. The generated handover document is saved on the server and is ready to be provided to the successor.

[1564] Emotion recognition by emotion engine

[1565] The server analyzes the text and voice information entered by the user using an emotion engine (e.g., IBM Watson Emotion Analysis). For example, the server recognizes the user's emotional state, such as stress or fatigue, during the handover process and records it in a database within the server. The server also analyzes the text and voice information using the following prompt:

[1566] "Analyze and record the user's emotional state based on text and voice input."

[1567] Adjusting work suggestions based on emotional state

[1568] The server adjusts its task suggestions based on the user's recorded emotional state: for example, if the user is stressed, the server may present only high-priority tasks or suggest postponing some tasks.

[1569] Responding to user questions

[1570] The user (successor) asks the server a question about work. For example, "Please tell me the format of the monthly report." The server generates a specific answer by referring to the generation AI and past databases and presents it to the user. The specific prompt is as follows:

[1571] "Can you give me an example of a monthly report and the format?"

[1572] Enhanced recommendations with an emotion engine

[1573] The server then analyzes the user's emotional state again and strengthens its work suggestions. For example, if the user is fatigued, the emotion engine will instruct the server to "suggest a short break" to improve work efficiency.

[1574] Specific operation example

[1575] 1. The user requests the server to collect business tasks.

[1576] 2. The server collects task information from mailboxes and task management tools and stores it in a database.

[1577] 3. The server uses the generation AI to generate the business manual and stores it on the server.

[1578] 4. The server creates a detailed handover document based on the generated business manual and stores it on the server.

[1579] 5. The server analyzes the information entered by the user using an emotion engine and records the recognized emotional state in a database.

[1580] 6. The server adjusts and presents task suggestions to the user based on the user's emotional state.

[1581] 7. The user asks a question, and the server uses generative AI to generate an answer and presents it to the user.

[1582] 8. The server proposes measures to reduce the workload based on the user's emotional state, thereby improving work efficiency.

[1583] This provides a system that enables highly efficient task handover through task organization, manual generation, handover document creation, and question response. By incorporating the user's emotional state, it is possible to make more human-friendly task proposals, aiming to further improve the stability and efficiency of tasks.

[1584] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1585] Step 1:

[1586] The user requests the collection of work tasks. The user inputs the command "Collect work tasks" to the server. The server receives this input and begins collecting work tasks from the user's mailbox or task management tool (e.g., Gmail, Trello). The collected task information (e.g., "Meeting with client," "Preparing monthly report," "Updating CRM system," etc.) is extracted and stored in a database on the server.

[1587] Step 2:

[1588] The server inputs data into the generative AI based on the task information collected in step 1. Specific task information is used as input. A generative AI model (e.g., OpenAI GPT-3) is used to generate detailed business manuals for each task. For example, the generative AI receives a prompt such as, "Please create a detailed business manual based on the collected task information." The output generated in response to this prompt is a business manual such as "Procedures for preparing for a meeting with a client," "Procedures for creating a monthly report," or "How to update the CRM system," which is stored on the server.

[1589] Step 3:

[1590] The server creates a handover document based on the business manual generated in step 2. First, it uses the business manual as input and automatically extracts and organizes important business details, execution methods, important points to note, contact information, etc. As a result of this data processing, a "handover document" is generated. The handover document contains detailed business details and is saved on the server.

[1591] Step 4:

[1592] The server sends the text and voice information entered by the user to an emotion engine (e.g., IBM Watson Emotion Analysis). The user's text and voice information are used as input, and the emotion engine analyzes them. The output of the emotion engine is the user's emotional state (e.g., stress, excitement, fatigue, etc.) and is recorded in a database on the server.

[1593] Step 5:

[1594] The server adjusts the task suggestions based on the user's emotional state recorded in step 4. Using the emotional state data as input, the server prioritizes the tasks based on this data. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks. The adjusted results are then presented to the user.

[1595] Step 6:

[1596] The user sends a business-related question to the server. For example, the user might input a question like, "What is the format of a monthly report?" The server receives this input and inputs the question into the generation AI. The prompt text is, "Please tell me an example of a monthly report and its format." The generation AI generates an appropriate answer, and this output is returned to the server. Finally, the appropriate answer is provided to the user.

[1597] Step 7:

[1598] The server then re-analyzes the user's emotional state and enhances its work suggestions. The emotional state data is used as input, and the server makes optimal work suggestions to the user based on the results of the emotion engine. For example, if the user is fatigued, the emotion engine outputs a command to the server saying, "Please suggest a short break." The server then provides this suggestion to the user, aiming to improve work efficiency.

[1599] Through these processing steps, the system can efficiently and accurately carry out task handover. Furthermore, by taking the user's emotional state into consideration, it is possible to propose tasks that are more human-friendly, thereby improving the stability and efficiency of tasks.

[1600] (Application example 2)

[1601] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] In systems aimed at improving work handover and work efficiency, suggestions that do not take into account the emotional state of employees can increase employee stress and fatigue. Furthermore, when employees ask questions about unclear work content, they may not receive appropriate answers quickly. To address these issues, a system that recognizes employees' emotions in real time and generates work suggestions and answers is needed.

[1603] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1604] In this invention, the server includes means for automatically organizing work content using a generation AI, means for automatically creating a handover document from a predecessor to a successor, means for proposing an appropriate way to handle new work by referring to past response history, means for recognizing employee emotions in real time and adjusting work proposals based on this, means for generating appropriate answers when employees ask questions about work, and means for proposing measures to reduce workload and prioritization based on emotions. This enables work handover that takes into account the emotional state of employees and improves work efficiency.

[1605] "Generative AI" is artificial intelligence that automatically generates content based on collected data.

[1606] "Organizing work content" is the process of classifying collected work tasks and compiling detailed procedures and points to note.

[1607] "Automatic handover document creation" is a function that automatically creates a document detailing the work to be handed over from the predecessor to the successor.

[1608] "A means of proposing appropriate ways to respond to new tasks by referring to past response history" is a function that proposes the optimal response to new tasks based on data from past tasks.

[1609] "Emotion recognition" is the process of analyzing an employee's text or voice data to understand their emotional state.

[1610] "Adjustment of work proposals" is a function that changes the content and order of work presented based on the employee's emotional state.

[1611] "Generating answers to questions" is the process of providing the most appropriate answer based on past data when an employee asks a question about something they don't understand at work.

[1612] "Workload reduction measures" are methods for reducing workload by adjusting the amount and importance of work, taking into account the emotional state of employees.

[1613] This invention is a system that uses generative AI to automatically organize the work content in a logistics center and combines it with an emotion engine that recognizes the emotions of employees. This system is mainly composed of a server, terminals, and users.

[1614] System configuration and operation

[1615] 1. Collecting business details

[1616] The user requests the server to collect business tasks. Specifically, the user sends an instruction to the server to "collect business tasks."

[1617] The server collects business tasks from users' mailboxes and task management tools. This includes task information such as inventory checks, shipping, etc. The collected task information is stored in a database on the server.

[1618] 2. Organizing work content and creating manuals

[1619] The server uses a generative AI model (such as TextGenie) to organize the work content based on the collected task information. It then generates a work manual that includes detailed procedures and points to note for each task. For example, it generates "procedures for inventory entry and retrieval work," "procedures for inventory confirmation," and "procedures for shipping work."

[1620] 3. Automatic creation of handover documents

[1621] The server automatically creates a handover document based on the generated business manual. The handover document includes important business details, response methods, contact information, etc., and the generated handover document is saved on the server.

[1622] 4. Emotion Recognition by Emotion Engine

[1623] The server uses an emotion engine (e.g., EmotionEngine) to recognize the user's emotions in real time. Based on the text or voice information entered by the user, the server analyzes the user's emotional state (e.g., stress, excitement, fatigue, etc.).

[1624] 5. Adjusting work suggestions based on emotional state

[1625] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server may suggest only high-priority tasks or suggest postponing some tasks.

[1626] 6. Responding to user questions

[1627] The user asks the server a question about a task they are unsure about. The server then references a database of past task manuals and handover documents and uses a generative AI model to generate an appropriate answer. This allows the user to quickly obtain a specific answer.

[1628] 7. Enhanced recommendations with an emotion engine

[1629] The server takes the user's emotional state into account to make more appropriate work suggestions. For example, if the user indicates fatigue, it will suggest ways to reduce the workload. The generated answers are provided to the user, providing appropriate guidance.

[1630] Specific operation example

[1631] Collection of business details

[1632] The user requests the server to collect business tasks.

[1633] The server collects tasks such as "warehouse entry and exit operations," "inventory checks," and "shipping operations" from users' mailboxes and task management tools and stores them in a database.

[1634] Organizing work content and creating manuals

[1635] The server uses AI generation to generate detailed business manuals based on the collected tasks, such as "procedures for receiving and retrieving goods," "procedures for inventory checks," and "procedures for shipping work." The generated business manuals are stored on the server.

[1636] Automatic creation of handover documents

[1637] The server uses the generated work manual to automatically create a detailed handover document, which includes the required work content, how to perform it, important points to note, contact information, etc., and is saved on the server.

[1638] Emotion recognition and recording by emotion engine

[1639] The server recognizes the emotions of the user in real time during the handover process. For example, if the user feels stressed during the handover, the emotion engine will grasp that emotion and record it on the server.

[1640] Adjusting work suggestions based on emotional state

[1641] The server recognizes that the user is under stress and presents only high-priority tasks or suggests postponing some tasks.

[1642] Responding to user questions

[1643] The user asks, "Please tell me the procedure for loading and unloading."

[1644] The server references a database of past work manuals and handover documents, and uses a generative AI to generate an appropriate answer regarding "procedures for warehousing and shipping work." The answer is presented to the user, providing specific procedural guidelines.

[1645] Enhanced recommendations with an emotion engine

[1646] The server considers the user's level of fatigue and suggests ways to reduce the workload, such as suggesting that the user take a short break, in order to improve work efficiency.

[1647] Prompt Sentence Examples

[1648] By inputting the following prompt sentence into the generative AI model, the necessary business manual will be generated.

[1649] Please generate an operational manual for a logistics center. The collected tasks are as follows:

[1650] 1. Incoming and outgoing work

[1651] 2. Check inventory

[1652] 3. Shipping

[1653] Include detailed steps and notes for each task.

[1654] In this way, by using the system of the present invention, it is possible to significantly improve the efficiency of work within a logistics center. In addition, by making suggestions that take into account the emotional state of employees, it is possible to reduce the burden on employees and provide a comfortable working environment.

[1655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1656] Step 1:

[1657] The user requests the server to collect business tasks. The user sends an instruction to the server via their terminal to "collect business tasks." Upon receiving this input, the server accesses the configured mailbox and task management tool to collect data on business tasks (e.g., warehousing and shipping work, inventory checks, shipping work). As an output, the collected task information is saved in the server's database.

[1658] Step 2:

[1659] The server organizes business tasks and generates a business manual that includes detailed procedures and important points. Based on the collected task information, the server uses a generative AI model (e.g., textgenie) to classify business tasks and generate specific procedures for each task. For example, it might include "detailed procedures for inventory entry and retrieval work" and "important points for inventory checks." Based on this input process, the organized business content is output as a text-format business manual and saved on the server.

[1660] Step 3:

[1661] The server automatically creates handover documents based on the generated business manuals. The server extracts important points from each business manual and automatically generates a series of handover documents. The handover documents include the job content, procedures, response methods, contact information, etc., and are recorded in the server's database. The generated handover documents are then ready to be provided to the new employee.

[1662] Step 4:

[1663] The server recognizes the user's emotional state in real time. The user inputs emotional data via text or voice through the device. The server analyzes this input data using an emotion engine (e.g., EmotionEngine) to determine the user's emotional state (e.g., stress, excitement, fatigue). This emotional state data is stored on the server.

[1664] Step 5:

[1665] The server adjusts task suggestions based on the user's emotional state. For example, if the user is stressed, the server will suggest only high-priority tasks or postpone some tasks. Based on this adjustment suggestion, a task priority list is generated and sent to the user's device.

[1666] Step 6:

[1667] The user requests a question about work from the server. The user sends a specific question (e.g., "Please tell me the procedure for warehouse entry and exit work") to the server via their device. The server receives this input data, references a database of past work manuals and handover documents, and uses a generative AI model to generate the optimal answer. The generated answer is sent to the user's device in text format.

[1668] Step 7:

[1669] The server takes the user's emotional state into account to make more appropriate work suggestions. The server reanalyzes the emotional data and, if the user indicates fatigue, generates work load reduction measures, such as suggesting a short break. These specific suggestions are displayed in text format on the user's device.

[1670] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1671] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1672] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1673] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1674] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1675] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1676] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1677] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1678] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1679] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1680] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1681] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1682] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1683] 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.

[1684] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1685] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1686] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1687] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1688] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1689] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1690] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1691] The following is further disclosed regarding the above embodiment.

[1692] (Claim 1)

[1693] A means of automatically organizing work content using generative AI,

[1694] A means for automatically creating a handover document from a predecessor to a successor;

[1695] A system that includes a means for proposing appropriate ways to respond to new tasks by referencing past response history.

[1696] (Claim 2)

[1697] 10. The system of claim 1, further comprising means for collecting predecessor job descriptions.

[1698] (Claim 3)

[1699] 10. The system of claim 1, further comprising means for recording and storing the generated business manual and handover document.

[1700] "Example 1"

[1701] (Claim 1)

[1702] A means of automatically organizing work content using generative AI,

[1703] A means for automatically creating a handover document from a predecessor to a successor;

[1704] A means of proposing appropriate ways to respond to new tasks by referring to past response history;

[1705] A means for collecting business tasks based on a user's instruction;

[1706] A means for storing the collected business tasks in a database;

[1707] A method for generating business manuals using AI based on business tasks stored in a database;

[1708] A means for automatically creating a handover document based on the generated business manual;

[1709] means for generating appropriate answers to user questions;

[1710] A system including:

[1711] (Claim 2)

[1712] 10. The system of claim 1, further comprising means for collecting predecessor job descriptions.

[1713] (Claim 3)

[1714] 10. The system of claim 1, further comprising means for recording and storing the generated business manual and handover document.

[1715] "Application Example 1"

[1716] (Claim 1)

[1717] A means of automatically organizing work content using generative AI,

[1718] A means for automatically creating a handover document from a predecessor to a successor;

[1719] A means of proposing appropriate ways to respond to new tasks by referring to past response history;

[1720] A means for collecting the operation history, work logs, and sensor data of factory robots to manage work tasks;

[1721] A system including a means for generating a business manual based on collected robot business content.

[1722] (Claim 2)

[1723] 10. The system of claim 1, further comprising means for collecting predecessor job descriptions.

[1724] (Claim 3)

[1725] 10. The system of claim 1, further comprising means for recording and storing the generated business manual and handover document.

[1726] "Example 2: Combining Emotion Engines"

[1727] (Claim 1)

[1728] A means of automatically organizing work content using generative AI,

[1729] A means for automatically creating a handover document from a predecessor to a successor;

[1730] A means of proposing appropriate ways to respond to new tasks by referring to past response history;

[1731] means for adjusting task suggestions based on the user's emotional state, the means including an emotion engine for analyzing the user's emotions;

[1732] A system that includes a means of generating appropriate answers to user questions by referencing a past database and using generative AI.

[1733] (Claim 2)

[1734] 10. The system of claim 1, further comprising means for collecting predecessor job descriptions.

[1735] (Claim 3)

[1736] 10. The system of claim 1, further comprising means for recording and storing the generated business manual and handover document.

[1737] "Application example 2 when combining emotion engines"

[1738] (Claim 1)

[1739] A means of automatically organizing work content using generative AI,

[1740] A means for automatically creating a handover document from a predecessor to a successor;

[1741] A means of proposing appropriate ways to respond to new tasks by referring to past response history;

[1742] A means to recognize employee sentiment in real time and adjust work recommendations accordingly;

[1743] A means of generating appropriate answers when employees ask work-related questions;

[1744] A system that includes a means of suggesting workload reduction measures and prioritization based on emotions.

[1745] (Claim 2)

[1746] 10. The system of claim 1, further comprising means for collecting predecessor job descriptions.

[1747] (Claim 3)

[1748] 10. The system of claim 1, further comprising means for recording and storing the generated business manual and handover document. [Explanation of symbols]

[1749] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically organizing work content using generative AI, A means for automatically creating a handover document from a predecessor to a successor; A system that includes a means for proposing appropriate ways to respond to new tasks by referencing past response history.

2. The system of claim 1 further comprising means for collecting predecessor job descriptions.

3. The system of claim 1 , further comprising means for recording and storing the generated business manual and handover document.

Images