system

Abstract

We provide the system. [Solution] A means of receiving authentication information from users and establishing access to multiple tools, A means for collecting business-related logs from the aforementioned tool, A generation model means that analyzes collected logs and generates relevant business information, A means for automatically generating a handover document based on the business information generated by the aforementioned generation model means, A means to present the user with a handover document and allow them to review and correct it, A means of sending the revised handover document to the successor, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern office work, the task handover process is very complicated. Especially during personnel changes or long-term vacations, the handover is often not properly carried out. In such a situation, there is a problem that the new person in charge cannot smoothly take over the work, and the work efficiency is significantly reduced. Also, since the handover work itself is regarded as non-productive work, it may also have an adverse impact on the motivation of employees.

Means for Solving the Problems

[0005] This invention is a technology that utilizes a generative AI model to collect work logs from various tools used in a user's daily work, analyzes these logs, and automatically organizes and integrates the information necessary for handover. Specifically, it uses a generative model that enables access to multiple tools based on authentication information from the user, automatically collects logs from the tools, and analyzes the acquired logs. Based on the analysis results, it automatically generates a handover document, presents it to the user for review and modification, and sends the final version to the successor, thereby enabling an efficient handover.

[0006] A "user" is an individual or organizational representative who uses the system and is responsible for providing and handing over work logs.

[0007] "Authentication information" refers to credentials such as IDs and passwords that a user provides to access a system, and is used to identify a specific individual or organization.

[0008] "Tools" refers to software applications used by users in their daily work, such as email, calendars, communication tools, video conferencing software, spreadsheet software, and presentation software.

[0009] "Work logs" refer to data and records related to a user's work activities performed through a tool, and are used to track the flow and content of administrative tasks.

[0010] A "generative model" is an artificial intelligence technology or algorithm that analyzes data based on acquired business logs and generates information related to business operations.

[0011] A "handover document" is a document that summarizes the job duties, tasks, and procedures from the previous person to the successor, and is created with the aim of ensuring the continuous performance of the job.

[0012] A "successor" refers to an individual or organizational person who takes over the responsibilities and newly assumes the duties. [Brief explanation of the drawing]

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

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

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

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of this invention aims to streamline the handover process by collecting work logs from multiple tools that users use on a daily basis, then analyzing them using a generation AI model, and automatically generating handover documents.

[0035] The server accesses the tool's API based on the authentication information provided by the user and collects the necessary business logs from each tool. This eliminates the need for users to manually collect information directly from the tools and ensures that all data is collected without omission.

[0036] The acquired work logs are stored in a database on the server, organized, and then analyzed by a generating AI model. Based on the log content, the AI ​​model extracts information related to the work (task progress, important matters, relevant parties, etc.) and organizes the priority and relationships of each task.

[0037] The analyzed information is automatically generated by the server as a formatted handover document. This handover document is presented to the user on their terminal. The user can review this presented handover document and modify its contents as needed. The modified handover document is then checked again by the server and automatically distributed to the successor via email or other means.

[0038] For example, if a user uses Gmail to send many emails related to project negotiations, the server retrieves the email content and associated calendar information, and the AI ​​model analyzes it to produce results such as "Important Announcements for Project Y." This information is automatically recorded in the handover document as "Contact Person for Project Y: Mr. A, Next Deadline: October 20th."

[0039] In this way, the present invention aims to improve the overall operational efficiency of the organization by significantly reducing the effort and errors involved in handing over tasks, thereby effectively utilizing the labor of employees.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user logs into the system and enters authentication credentials to grant access to various tools. The server uses these credentials to establish a session to access APIs for Gmail, Calendar, chat tools, video conferencing tools, spreadsheet software, and presentation software.

[0043] Step 2:

[0044] The server periodically collects work logs related to the user's daily tasks from various tools. This collection includes email sending and receiving history, calendar schedules, chat conversation history, video conference participation records, and spreadsheet data update history. The collected data is stored in the server's database.

[0045] Step 3:

[0046] The server inputs the collected work logs into a generating AI model, which then performs an analysis of the work content. The generating AI model determines the priority, relevance, and progress of tasks from the log data and extracts relevant work information. For example, it identifies tasks belonging to a specific project, key contacts, and incomplete tasks.

[0047] Step 4:

[0048] The server automatically generates a draft of the handover document based on the analysis results. This handover document is formatted to include essential elements such as important work details, deadlines, information on the people involved, and the next tasks to be performed.

[0049] Step 5:

[0050] A draft of the handover document is displayed on the device, and the user reviews its contents. The user can check the accuracy of the contents and enter any necessary corrections or additions. Once corrections are complete, the user presses the "Confirm Handover Document" button.

[0051] Step 6:

[0052] The server sends the handover document, which has been revised and finalized by the user, to the successor. This is usually done via email or other means. The successor receives the finalized handover document on their device, allowing them to immediately access the information necessary to start their duties.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] Because fragments of information are scattered across various information processing devices, there are challenges such as important information being lost or integration being difficult during work handovers. This hinders efficient work handovers between employees and causes problems with work continuity.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing devices, means for collecting business-related records from the information processing devices, and means for generating a model for analyzing the collected records and generating relevant business information. This enables the integration of information and the unified management of important business information.

[0058] "Authentication information" refers to identification information required when a user accesses an information processing device, and includes, for example, a username, password, and authorization token.

[0059] An "information processing device" refers to a system or application for processing and managing digital data, and is used for communication and information management purposes.

[0060] "Records" refer to a collection of data that shows the process and results of work, including emails, meeting minutes, and task management information.

[0061] A "generative model" refers to an algorithm or artificial intelligence that analyzes collected data and automatically generates information aligned with a specific purpose.

[0062] A "report" is a document automatically generated based on the information that has been collected, and its purpose is to facilitate the handover of tasks and information sharing.

[0063] The "onboarding process" refers to the series of training and procedures required when a new person joins an organization or project.

[0064] The system of this invention is designed to streamline the handover process for business operations. The server establishes access to the API of an information processing device (e.g., an email system or a calendar management system) by receiving authentication information provided by the user. Specific examples include Google Workspace® and Microsoft 365®. The server collects necessary records from these information processing devices, reliably gathering data and eliminating the effort required for manual data collection.

[0065] The acquired records are stored in a database on the server and organized appropriately. The server uses a generative AI model to analyze the stored records and extract information relevant to the work. The generative AI model analyzes the progress of tasks and relevant important matters from each record and automatically generates reports based on the extracted information.

[0066] For example, if a user is conducting important communication within a project, the server uses the Gmail API to collect relevant emails and retrieves meeting information from Google Calendar. The generative AI model extracts "important communications regarding Project X" from these records and formats them into a report.

[0067] Users can review reports displayed on their terminals and make corrections as needed. The corrected reports are then reviewed by the server and automatically distributed to their successors via email or other means. This significantly reduces the effort and errors involved in handing over tasks, improving overall organizational efficiency.

[0068] An example of a prompt for a generating AI model is, "Extract important information related to Project X from Gmail and Calendar, and create a report." Based on this prompt, the AI ​​model analyzes the business information and integrates the necessary information into a report.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server receives authentication information from the user. Based on this input information, the server establishes access to the APIs of each information processing device. Specifically, it uses the username and password entered by the user on the terminal to obtain an authentication token for the Google Workspace API and establishes a connection to the server. This ensures the communication channel necessary for subsequent data collection processes.

[0072] Step 2:

[0073] The server collects business-related records from information processing devices through established APIs. At this stage, email content and calendar events are retrieved as input data. The server retrieves business-related emails via the Gmail API and further collects data on related meetings and events using the Google Calendar API. This allows integrated business data to be stored in the server's database.

[0074] Step 3:

[0075] The server stores the collected records in a database and prepares to input the data into the generating AI model. This input work record is then analyzed by the AI ​​model. The server sends an analysis request to the generating AI model using pre-configured prompts, such as "Extract important tasks related to Project X from meeting information and email history." The AI ​​model then extracts and returns the task progress and other relevant important information.

[0076] Step 4:

[0077] Based on the analysis results obtained from the generated AI model, the server automatically generates a report. This generated report outputs the importance and progress of each task in an organized manner. The server receives the results and creates a report based on a format such as "Project X: Assignee, Deadline, Key Points." This makes it easy for the user to review the contents.

[0078] Step 5:

[0079] On the terminal, the user can review the generated report and make corrections as needed. The input is the user's edits to the report, allowing them to directly adjust the report's text on the terminal. The user then sends the final, revised report to the server as output, preparing it for distribution to their successor.

[0080] Step 6:

[0081] The server reviews the report corrected by the user and automatically sends it to the successor. This final output is a PDF document sent to the successor's mailbox. The server uses the default email service to email the revised report to the successor's address. This process ensures a smooth handover and maintains continuity.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] In manufacturing environments, the handover of tasks during shift changes is often inefficient. Insufficient information sharing leads to unclear progress tracking, hindering efficient work. Furthermore, the time-consuming process of verifying and correcting information places a heavy burden on workers. Therefore, there is a need to automate the handover process in factories and provide a means for rapid and accurate information sharing.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems, means for collecting log data related to business operations from the information processing systems, and means for generating a generative artificial intelligence model that analyzes the collected log data and generates relevant business information. This makes it possible to automate the handover of tasks and effectively share information even when work shifts change in a manufacturing system.

[0087] A "user" refers to a worker who accesses an information system and takes over tasks.

[0088] "Authentication information" refers to the information a user needs to log in to an information processing system, and typically includes a username and password.

[0089] An "information processing system" is a computer system used for inputting, processing, and storing data related to business operations.

[0090] "Log data" refers to data that records the operation history of an information processing system and the progress of business operations.

[0091] A "generative artificial intelligence model" is an artificial intelligence technology that analyzes collected data and extracts and generates information necessary for business operations.

[0092] A "handover document" is a document that details the tasks and their progress, and its purpose is to enable a successor to quickly take over the responsibilities.

[0093] An "electronic information terminal" is an electronic device capable of displaying and inputting information, and refers to portable devices such as smartphones and tablets.

[0094] A "manufacturing system" is a set of machinery, equipment, and management systems used to produce a product.

[0095] This invention is implemented as a system to streamline the handover of tasks in manufacturing facilities. This system primarily consists of a server, an electronic information terminal, and multiple information processing systems. The server receives authentication information from the user and establishes access to the information processing systems. Using this authentication information, the server automatically collects task-related log data from information processing systems such as manufacturing management systems and quality control systems. At this stage, human intervention can be minimized.

[0096] The server implements a generative artificial intelligence model to analyze collected log data. The purpose of the analysis is to extract business information and generate work handover documents. This generated data is presented to the user via an electronic information terminal. The user can review the work handover document on the electronic information terminal and make corrections as needed. The corrected information is then shared with the next work shift via the server.

[0097] As a concrete example, during the shift change from night to day on a manufacturing line, this system automatically incorporates information about the previous night's work progress and machine status into a handover document, enabling the next shift's workers to quickly begin their duties. The generating AI model utilizes the latest technology, such as OpenAI®, demonstrating extensive analytical capabilities. An example of a prompt message is, "Analyze the following logs to create a summary of the work performed: including machine A's operating time, maintenance records, and parts inventory status." This ensures continuity of operations and improves work efficiency.

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The server receives authentication information from the user and establishes access to the information processing system. It receives the authentication information as input and uses it to connect to various information processing systems via APIs. Once the connection is established, access to business-related data becomes possible.

[0101] Step 2:

[0102] The server collects log data related to business operations from connected information processing systems. It collects data such as operating hours and maintenance records from these systems as input, integrates this data, and stores it in a database. This log data collection prepares the basic data necessary for subsequent analysis.

[0103] Step 3:

[0104] The server inputs the collected log data into a generating AI model, which extracts and analyzes business information. In this process, the generating AI model analyzes the input data and outputs the progress of the work and important points as a result. This identifies the necessary business information, which is then used in the next step.

[0105] Step 4:

[0106] The server automatically generates a handover document based on the analyzed business information. It uses business information obtained from the AI ​​model as input and outputs the handover document according to a standardized format. This output is in a format that is easy for the next worker to understand.

[0107] Step 5:

[0108] The terminal displays the generated handover document to the user. The user can review the handover document on the terminal and make corrections as needed. The input is the automatically generated handover document, and the output is the document after the user has made corrections.

[0109] Step 6:

[0110] The server sends the user-revised handover document to the successor. It receives the revised handover document as input and forwards it to the successor's device via email or cloud storage. This process ensures a reliable and rapid continuity of work.

[0111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0112] This invention is a system that streamlines the handover of tasks and incorporates an emotion engine to consider the user's emotions during the handover process. This system collects work logs from various tools that users use on a daily basis, analyzes them using a generated AI model, and then uses the emotion engine to support the optimization of the handover.

[0113] The server uses user authentication credentials to establish connections to multiple tools (such as email, scheduling, and communication tools) and automatically collects work-related log data from them. The collected data is analyzed by a generative AI model to provide a detailed analysis of the user's work. The analysis results include the progress of key tasks and important future considerations.

[0114] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The emotion engine uses user input and responses on the tool, and in some cases biometric data, to infer the user's emotions. The results of the emotion engine's analysis are reflected in the generation of the handover document, and the content is adjusted according to the user's psychological state. For example, if the emotion engine determines that the user's stress level is high, the handover document will be generated in a more concise form that emphasizes important points.

[0115] After the handover document is generated, it is displayed on the terminal. The user can review this handover document and make corrections as needed. Once the user has completed their corrections, the final version is confirmed, and the server sends this confirmed handover document to the successor. The transmission method is email or similar, and the successor can smoothly begin their duties based on the received handover document.

[0116] As a concrete example, in a task requiring project management, if the emotion engine on the terminal detects signs of "fatigue" from the user, the handover document will be updated to readjust the task priorities, with tasks requiring attention being postponed. In this way, the present invention realizes efficient task handover that takes into account the user's emotions.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The user logs into the system and provides their authentication credentials. This allows the server to prepare to establish access to multiple business tools (email, calendar, chat, etc.).

[0120] Step 2:

[0121] The server uses established access rights to collect log data related to the user's work from each tool. This data includes email subjects, conversation history, and calendar events. The collected data is stored in a database.

[0122] Step 3:

[0123] The server inputs the collected work logs into an AI model for detailed analysis of the work content. This analysis extracts important information such as task priorities, progress, and relationships.

[0124] Step 4:

[0125] The server collects user emotional data and inputs it into the emotion engine. The emotion engine analyzes the tone of the user's input and their behavior patterns during work hours to identify the user's current emotional state.

[0126] Step 5:

[0127] The server integrates the analysis results of the generated AI model and the results of the emotion engine, and automatically generates a handover document with appropriate adjustments. The way information is presented and the level of detail are adjusted according to the user's emotional state.

[0128] Step 6:

[0129] The generated transfer document is displayed on the device, and the user reviews its contents. The user can make corrections to the transfer document as needed.

[0130] Step 7:

[0131] Once the user completes the revisions, the terminal notifies the server that the handover document has been reviewed. The server then sends the final version of the handover document to the successor. The successor can then begin their work based on the received handover document.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] For efficient job handover, it's crucial not only to communicate job details but also to consider the user's feelings and emotional state. However, conventional systems struggle to automatically create handover documents that reflect the user's emotions, ultimately hindering smooth job transitions. Furthermore, they often fail to adequately integrate data from different information processing devices or automatically adjust job priorities. Therefore, there is a need for a system that enables more efficient handover while also considering emotional aspects.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes means for receiving authentication information from a user and establishing access to an information processing device, means for collecting business-related records from the information processing device, and means for automatically generating a handover document based on sentiment analysis results. This makes it possible to automatically create a handover document that reflects the user's sentiment while integrating data from different information processing devices.

[0137] "Authentication information" refers to information used to identify a user and grant them permission to access information processing equipment.

[0138] "Information processing equipment" refers to a set of tools that provide business-related data, including email, schedule management, and communication tools.

[0139] "Records" refer to business-related data and logs collected from information processing equipment.

[0140] "Generative model means" refers to a model within a system that analyzes collected records and generates business information and task priorities.

[0141] A "handover document" is a document automatically generated by a generative model to communicate the details of the work to the next person in charge.

[0142] "Emotional analysis results" refer to the results of analyzing user input data and responses to evaluate the user's emotional state.

[0143] "Next person in charge" refers to the person who takes over the duties and becomes responsible for those duties.

[0144] This invention is a system for facilitating smooth business handover. It collects business-related records from multiple tools and analyzes them using a generative AI model. Based on the analysis results and user sentiment analysis results, it generates an optimized handover document. Specifically, the server uses user authentication information to access information processing devices such as email, schedule management, and communication tools. This allows the server to collect records necessary for the business and analyze that data in detail using the generative model.

[0145] The generative AI model used here analyzes collected data using natural language processing techniques to determine the importance and priority of related tasks. Furthermore, an emotion engine with sentiment analysis capabilities evaluates the user's emotional state, and the results are reflected in the content of the handover document. For example, if the user is determined to be in a state of "fatigue," the handover document will be adjusted to highlight important points and postpone tasks that require attention.

[0146] The handover document displayed on the terminal can be reviewed and modified by the user. After the user makes a final confirmation, the server sends the revised document to the next person in charge. Based on this information, the next person in charge can start their work quickly and smoothly.

[0147] As a concrete example, in tasks requiring project management, the server retrieves task progress data from a scheduling tool. If the emotion engine assesses the user's stress level as high, the generated handover document is simplified, and the priority of critical tasks is further emphasized. An example of a prompt to the generating AI model would be: "Analyze the user's work log and generate a handover document that takes into account the progress and emotional state of key tasks. If the user's current stress level is high, readjust the priorities."

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The server establishes access to the information processing device using the user's authentication information. This access includes securely logging into various tools using API keys or the OAuth protocol. User authentication information is required as input, and the output is a state where access to the various tools is permitted.

[0151] Step 2:

[0152] The server collects business-related records from information processing devices with established access. Specifically, the server retrieves emails, schedule information, chat history, etc., via APIs. The input for this step is having access rights, and the output is the various business-related data that has been collected.

[0153] Step 3:

[0154] The server inputs the collected data into a generative AI model, which then analyzes the business processes. Here, the generative AI model uses natural language processing techniques to extract task progress and key information. A prompt is given to the model, and the analyzed business information is generated as output. Specifically, a prompt such as "Please extract task progress and key information" is input.

[0155] Step 4:

[0156] The server uses an emotion engine to analyze the user's input history and responses, and evaluates their emotional state. Specific operations include text tone analysis and biometric data analysis. The input for this step is user behavior data, and the output is the analyzed emotional state.

[0157] Step 5:

[0158] The server automatically generates handover documents based on analyzed business information and emotional states. The server integrates this data and combines the information in a way that is optimal for the user. The input to this process is business information and emotional states, and the output is the handover document.

[0159] Step 6:

[0160] The handover document is displayed on the terminal, and the user reviews its contents and makes any necessary corrections. Specifically, the user can modify the document's content using an editor. The input is an automatically generated handover document, and the output is the document reviewed and modified by the user.

[0161] Step 7:

[0162] Once the user has finished reviewing the document, the server sends the final version of the handover document to the next person in charge. This transmission is done via email or in-system messaging. The input is the reviewed final version of the document, and the output is the status of successful transmission.

[0163] (Application Example 2)

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

[0165] Current business handover processes are inefficient due to a lack of information organization and emotional support. In particular, the failure to optimize the handover process while considering the user's emotions is a major cause of stress and work inefficiency. Therefore, there is a need for a system that enables smooth business handover while taking the user's emotional state into account.

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

[0167] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems; means for collecting records related to the work from the information processing systems; means for analyzing the collected records and generating a generation model for generating related work information; and means for using an emotion analysis engine to estimate the user's emotional state and reflect it in the handover document. This enables a more efficient and smoother handover of work that takes into account the user's emotions.

[0168] A "user" refers to an individual or group that uses the system to perform their work.

[0169] "Authentication information" refers to the identification information that a user uses to access a system.

[0170] An "information processing system" refers to a system that includes tools and applications for processing, storing, or transferring data.

[0171] "Records" refer to data that includes the history of activities and transactions related to business operations.

[0172] "Generative model means" refers to a mechanism that includes technologies and algorithms for analyzing collected data and generating business information.

[0173] An "emotion analysis engine" refers to a program or technology that infers a user's emotional state and reflects the analysis results in the system.

[0174] A "handover document" refers to a document created to communicate job duties, procedures, and important information to a successor.

[0175] To implement this invention, two main elements are important: a server and a user terminal. The server receives authentication information from the user and establishes access to multiple information processing systems. It then automatically collects business-related records from these information processing systems. The collected records are analyzed by a generative modeling means running on the server, and relevant business information is generated.

[0176] The analysis utilizes a generative AI model. This model incorporates algorithms to integrate data and determine the priority of tasks relevant to the work. The server also features an emotion analysis engine, which uses data from user input and responses to infer the user's emotional state. The results of this analysis are reflected in the handover documents.

[0177] Specifically, the handover document displayed on the user's device includes not only the generated work information but also adjustments based on the user's emotional state. For example, if user fatigue is detected, the priority and explanation of important tasks are automatically modified. Furthermore, the user can review the handover document on their device and make corrections as needed.

[0178] In terms of hardware, smartphones and computers will be used for data collection and display. The software will include the generative AI model "GPT-3 (registered trademark)" and an "emotion analysis engine." A cloud platform that allows these software components to operate efficiently is also desirable.

[0179] As a concrete example, a factory manager uses a smartphone to record the status of the production line, and a server collects and analyzes this data to generate a proposal for the tired manager to postpone high-load tasks. In this case, the AI ​​model that generates the proposal might use the following prompt: "Generate an optimized procedure for handing over production line check tasks when the user is tired."

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The server receives authentication information from the user and establishes access to the information processing system. The input here is the user's authentication information, and the output is the connection status to the system. This creates a state where the necessary data can be collected based on the user's access rights.

[0183] Step 2:

[0184] The server collects business-related records from information processing systems. The input is log data obtained from each system, and the output is a dataset of the collected records. The server retrieves the data via an API and stores it in a database for centralized management.

[0185] Step 3:

[0186] The server analyzes the collected data using a generative AI model and generates relevant business information. The input is the collected log data, and the output is the analyzed business information. Generative AI models such as "GPT-3" are used for the analysis, and task progress and priorities are extracted through prompt messages.

[0187] Step 4:

[0188] The server uses an emotion analysis engine to estimate the user's emotional state. The input is the user's responses and input data, and the output is the analysis result of the user's emotional state. The emotion engine utilizes biometric data to quantify the user's stress and fatigue.

[0189] Step 5:

[0190] The server automatically generates handover documents based on the generated business information and emotional state. The input is analysis results and emotional data, and the output is a customized handover document. The server generates the document while reflecting the business information and highlighting important items.

[0191] Step 6:

[0192] The terminal presents the generated handover document to the user, allowing for review and modification. The input here is the generated handover document, and the output is the document modified by the user. The user can view the document on the terminal and make any necessary corrections.

[0193] Step 7:

[0194] The server sends the revised handover document to the next person in charge. This input is the final, confirmed handover document, and the output is ready to be sent to the next person in charge. Email and cloud platforms are used to facilitate a smooth handover of duties.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0197] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0202] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0207] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0209] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0210] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0211] The system of this invention aims to streamline the handover process by collecting work logs from multiple tools that users use on a daily basis, then analyzing them using a generation AI model, and automatically generating handover documents.

[0212] The server accesses the tool's API based on the authentication information provided by the user and collects the necessary business logs from each tool. This eliminates the need for users to manually collect information directly from the tools and ensures that all data is collected without omission.

[0213] The acquired work logs are stored in a database on the server, organized, and then analyzed by a generating AI model. Based on the log content, the AI ​​model extracts information related to the work (task progress, important matters, relevant parties, etc.) and organizes the priority and relationships of each task.

[0214] The analyzed information is automatically generated by the server as a formatted handover document. This handover document is presented to the user on their terminal. The user can review this presented handover document and modify its contents as needed. The modified handover document is then checked again by the server and automatically distributed to the successor via email or other means.

[0215] For example, if a user uses Gmail to send many emails related to project negotiations, the server retrieves the email content and associated calendar information, and the AI ​​model analyzes it to produce results such as "Important Announcements for Project Y." This information is automatically recorded in the handover document as "Contact Person for Project Y: Mr. A, Next Deadline: October 20th."

[0216] In this way, the present invention aims to improve the overall operational efficiency of the organization by significantly reducing the effort and errors involved in handing over tasks, thereby effectively utilizing the labor of employees.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user logs into the system and enters authentication credentials to grant access to various tools. The server uses these credentials to establish a session to access APIs for Gmail, Calendar, chat tools, video conferencing tools, spreadsheet software, and presentation software.

[0220] Step 2:

[0221] The server periodically collects work logs related to the user's daily tasks from various tools. This collection includes email sending and receiving history, calendar schedules, chat conversation history, video conference participation records, and spreadsheet data update history. The collected data is stored in the server's database.

[0222] Step 3:

[0223] The server inputs the collected work logs into a generating AI model, which then performs an analysis of the work content. The generating AI model determines the priority, relevance, and progress of tasks from the log data and extracts relevant work information. For example, it identifies tasks belonging to a specific project, key contacts, and incomplete tasks.

[0224] Step 4:

[0225] The server automatically generates a draft of the handover document based on the analysis results. This handover document is formatted to include essential elements such as important work details, deadlines, information on the people involved, and the next tasks to be performed.

[0226] Step 5:

[0227] A draft of the handover document is displayed on the device, and the user reviews its contents. The user can check the accuracy of the contents and enter any necessary corrections or additions. Once corrections are complete, the user presses the "Confirm Handover Document" button.

[0228] Step 6:

[0229] The server sends the handover document, which has been revised and finalized by the user, to the successor. This is usually done via email or other means. The successor receives the finalized handover document on their device, allowing them to immediately access the information necessary to start their duties.

[0230] (Example 1)

[0231] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0232] Because fragments of information are scattered across various information processing devices, there are challenges such as important information being lost or integration being difficult during work handovers. This hinders efficient work handovers between employees and causes problems with work continuity.

[0233] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0234] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing devices, means for collecting business-related records from the information processing devices, and means for generating a model for analyzing the collected records and generating relevant business information. This enables the integration of information and the unified management of important business information.

[0235] "Authentication information" refers to identification information required when a user accesses an information processing device, and includes, for example, a username, password, and authorization token.

[0236] An "information processing device" refers to a system or application for processing and managing digital data, and is used for communication and information management purposes.

[0237] "Records" refer to a collection of data that shows the process and results of work, including emails, meeting minutes, and task management information.

[0238] A "generative model" refers to an algorithm or artificial intelligence that analyzes collected data and automatically generates information aligned with a specific purpose.

[0239] A "report" is a document automatically generated based on the information that has been collected, and its purpose is to facilitate the handover of tasks and information sharing.

[0240] The "onboarding process" refers to the series of training and procedures required when a new person joins an organization or project.

[0241] The system of this invention is designed to streamline the handover process for business operations. The server establishes access to the API of an information processing device (e.g., an email system or a calendar management system) by receiving authentication information provided by the user. Google Workspace and Microsoft 365 are examples of such devices. The server collects the necessary records from these information processing devices and reliably gathers data, thus eliminating the effort required for manual data collection.

[0242] The acquired records are stored in a database on the server and organized appropriately. The server uses a generative AI model to analyze the stored records and extract information relevant to the work. The generative AI model analyzes the progress of tasks and relevant important matters from each record and automatically generates reports based on the extracted information.

[0243] For example, if a user is conducting important communication within a project, the server uses the Gmail API to collect relevant emails and retrieves meeting information from Google Calendar. The generative AI model then extracts "important communications regarding Project X" from these records and formats them into a report.

[0244] Users can review reports displayed on their terminals and make corrections as needed. The corrected reports are then reviewed by the server and automatically distributed to their successors via email or other means. This significantly reduces the effort and errors involved in handing over tasks, improving overall organizational efficiency.

[0245] An example of a prompt for a generating AI model is, "Extract important information related to Project X from Gmail and Calendar, and create a report." Based on this prompt, the AI ​​model analyzes the business information and integrates the necessary information into a report.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The server receives authentication information from the user. Based on this input information, the server establishes access to the APIs of each information processing device. Specifically, it uses the username and password entered by the user on the terminal to obtain an authentication token for the Google Workspace API and establishes a connection to the server. This ensures the communication channel necessary for subsequent data collection processes.

[0249] Step 2:

[0250] The server collects business-related records from information processing devices through established APIs. At this stage, email content and calendar events are retrieved as input data. The server retrieves business-related emails via the Gmail API and further collects data on related meetings and events using the Google Calendar API. This allows integrated business data to be stored in the server's database.

[0251] Step 3:

[0252] The server stores the collected records in a database and prepares to input the data into the generating AI model. This input work record is then analyzed by the AI ​​model. The server sends an analysis request to the generating AI model using pre-configured prompts, such as "Extract important tasks related to Project X from meeting information and email history." The AI ​​model then extracts and returns the task progress and other relevant important information.

[0253] Step 4:

[0254] Based on the analysis results obtained from the generated AI model, the server automatically generates a report. This generated report outputs the importance and progress of each task in an organized manner. The server receives the results and creates a report based on a format such as "Project X: Assignee, Deadline, Key Points." This makes it easy for the user to review the contents.

[0255] Step 5:

[0256] On the terminal, the user can review the generated report and make corrections as needed. The input is the user's edits to the report, allowing them to directly adjust the report's text on the terminal. The user then sends the final, revised report to the server as output, preparing it for distribution to their successor.

[0257] Step 6:

[0258] The server reviews the report corrected by the user and automatically sends it to the successor. This final output is a PDF document sent to the successor's mailbox. The server uses the default email service to email the revised report to the successor's address. This process ensures a smooth handover and maintains continuity.

[0259] (Application Example 1)

[0260] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] In manufacturing environments, the handover of tasks during shift changes is often inefficient. Insufficient information sharing leads to unclear progress tracking, hindering efficient work. Furthermore, the time-consuming process of verifying and correcting information places a heavy burden on workers. Therefore, there is a need to automate the handover process in factories and provide a means for rapid and accurate information sharing.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0263] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems, means for collecting log data related to business operations from the information processing systems, and means for generating a generative artificial intelligence model that analyzes the collected log data and generates relevant business information. This makes it possible to automate the handover of tasks and effectively share information even when work shifts change in a manufacturing system.

[0264] A "user" refers to a worker who accesses an information system and takes over tasks.

[0265] "Authentication information" refers to the information a user needs to log in to an information processing system, and typically includes a username and password.

[0266] An "information processing system" is a computer system used for inputting, processing, and storing data related to business operations.

[0267] "Log data" refers to data that records the operation history of an information processing system and the progress of business operations.

[0268] A "generative artificial intelligence model" is an artificial intelligence technology that analyzes collected data and extracts and generates information necessary for business operations.

[0269] A "handover document" is a document that details the tasks and their progress, and its purpose is to enable a successor to quickly take over the responsibilities.

[0270] An "electronic information terminal" is an electronic device capable of displaying and inputting information, and refers to portable devices such as smartphones and tablets.

[0271] A "manufacturing system" is a set of machinery, equipment, and management systems used to produce a product.

[0272] This invention is implemented as a system to streamline the handover of tasks in manufacturing facilities. This system primarily consists of a server, an electronic information terminal, and multiple information processing systems. The server receives authentication information from the user and establishes access to the information processing systems. Using this authentication information, the server automatically collects task-related log data from information processing systems such as manufacturing management systems and quality control systems. At this stage, human intervention can be minimized.

[0273] The server implements a generative artificial intelligence model to analyze collected log data. The purpose of the analysis is to extract business information and generate work handover documents. This generated data is presented to the user via an electronic information terminal. The user can review the work handover document on the electronic information terminal and make corrections as needed. The corrected information is then shared with the next work shift via the server.

[0274] As a concrete example, during the shift change from night to day on a manufacturing line, this system automatically incorporates information about the previous night's work progress and machine status into a handover document, allowing the next shift's workers to quickly begin their duties. The generating AI model utilizes the latest technologies, such as OpenAI, demonstrating extensive analytical capabilities. An example of a prompt message is, "Analyze the following logs to create a summary of the work performed: including machine A's operating hours, maintenance records, and parts inventory status." This ensures continuity of operations and improves work efficiency.

[0275] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0276] Step 1:

[0277] The server receives authentication information from the user and establishes access to the information processing system. It receives the authentication information as input and uses it to connect to various information processing systems via an API. By establishing the connection, access to business-related data becomes possible.

[0278] Step 2:

[0279] The server collects business-related log data from the connected information processing system. It collects data such as operation time and maintenance records obtained from the information processing system as input and integrates and stores it in a database. By collecting this log data, the basic data required for subsequent analysis is prepared.

[0280] Step 3:

[0281] The server inputs the collected log data into the generated AI model, extracts and analyzes business information. In this process, the generated AI model analyzes the input data and outputs the progress status and important matters of the business as a result. Thereby, the necessary business information is identified and used in the next step.

[0282] Step 4:

[0283] The server automatically generates a business handover document based on the analyzed business information. It uses the business information obtained from the AI model as input and outputs the handover document according to a fixed format. This output is in a format that is easy for the next operator to understand.

[0284] Step ⑤:

[0285] The terminal presents the generated handover document to the user. The user can check the handover document on the terminal and make corrections if necessary. The input is the automatically generated handover document, and the output is the document after being corrected by the user.

[0286] Step 6:

[0287] The server sends the handover document corrected by the user to the successor. It receives the corrected handover document as input and transfers it to the successor's terminal via email or cloud storage. This process enables reliable and rapid business continuity.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0289] This invention is a system that incorporates an emotion engine for considering the user's emotion in the process of handover while improving the efficiency of business handover. This system collects business logs from various tools that the user uses daily, analyzes them using a generated AI model, and then utilizes the emotion engine to assist in optimizing the handover.

[0290] The server establishes connections to a plurality of tools (such as email, schedule management, communication tools, etc.) using the user's authentication information and automatically collects log data related to business from these. The collected data is analyzed by the generated AI model, and the user's business content is analyzed in detail. The analysis results include the progress of major tasks and important matters in the future, etc.

[0291] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The emotion engine uses user input and responses on the tool, and in some cases biometric data, to infer the user's emotions. The results of the emotion engine's analysis are reflected in the generation of the handover document, and the content is adjusted according to the user's psychological state. For example, if the emotion engine determines that the user's stress level is high, the handover document will be generated in a more concise form that emphasizes important points.

[0292] After the handover document is generated, it is displayed on the terminal. The user can review this handover document and make corrections as needed. Once the user has completed their corrections, the final version is confirmed, and the server sends this confirmed handover document to the successor. The transmission method is email or similar, and the successor can smoothly begin their duties based on the received handover document.

[0293] As a concrete example, in a task requiring project management, if the emotion engine on the terminal detects signs of "fatigue" from the user, the handover document will be updated to readjust the task priorities, with tasks requiring attention being postponed. In this way, the present invention realizes efficient task handover that takes into account the user's emotions.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The user logs into the system and provides their authentication credentials. This allows the server to prepare to establish access to multiple business tools (email, calendar, chat, etc.).

[0297] Step 2:

[0298] The server collects log data related to the user's work from each tool using established access rights. This data includes the subject of emails, conversation history, calendar schedules, etc. The collected data is stored in a database.

[0299] Step 3:

[0300] The server inputs the collected work logs into the generated AI model for a detailed analysis of the work content. Through this analysis, important information such as the priority, progress, and relevance of tasks is extracted.

[0301] Step 4:

[0302] The server collects the user's sentiment data and inputs it into the sentiment engine. The sentiment engine analyzes the tone of the user's input and the operation pattern during working hours to identify the user's current sentiment state.

[0303] Step 5:

[0304] The server integrates the analysis results of the generated AI model and the results of the sentiment engine, and automatically generates a handover document with appropriate adjustments. Depending on the user's sentiment state, the information presentation method and the detail level of the content are adjusted.

[0305] Step 6:

[0306] The generated handover document is displayed on the terminal, and the user checks the content. The user can modify the handover document as needed.

[0307] Step 7:

[0308] When the user completes the modification, the terminal notifies the server that the handover document has been confirmed. The server sends the final version of the handover document to the successor. The successor can start working based on the received handover document.

[0309] (Example 2)

[0310] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0311] For efficient job handover, it's crucial not only to communicate job details but also to consider the user's feelings and emotional state. However, conventional systems struggle to automatically create handover documents that reflect the user's emotions, ultimately hindering smooth job transitions. Furthermore, they often fail to adequately integrate data from different information processing devices or automatically adjust job priorities. Therefore, there is a need for a system that enables more efficient handover while also considering emotional aspects.

[0312] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0313] In this invention, the server includes means for receiving authentication information from a user and establishing access to an information processing device, means for collecting business-related records from the information processing device, and means for automatically generating a handover document based on sentiment analysis results. This makes it possible to automatically create a handover document that reflects the user's sentiment while integrating data from different information processing devices.

[0314] "Authentication information" refers to information used to identify a user and grant them permission to access information processing equipment.

[0315] "Information processing equipment" refers to a set of tools that provide business-related data, including email, schedule management, and communication tools.

[0316] "Records" refer to business-related data and logs collected from information processing equipment.

[0317] "Generative model means" refers to a model within a system that analyzes collected records and generates business information and task priorities.

[0318] A "handover document" is a document automatically generated by a generative model to communicate the details of the work to the next person in charge.

[0319] "Emotional analysis results" refer to the results of analyzing user input data and responses to evaluate the user's emotional state.

[0320] "Next person in charge" refers to the person who takes over the duties and becomes responsible for those duties.

[0321] This invention is a system for facilitating smooth business handover. It collects business-related records from multiple tools and analyzes them using a generative AI model. Based on the analysis results and user sentiment analysis results, it generates an optimized handover document. Specifically, the server uses user authentication information to access information processing devices such as email, schedule management, and communication tools. This allows the server to collect records necessary for the business and analyze that data in detail using the generative model.

[0322] The generative AI model used here analyzes collected data using natural language processing techniques to determine the importance and priority of related tasks. Furthermore, an emotion engine with sentiment analysis capabilities evaluates the user's emotional state, and the results are reflected in the content of the handover document. For example, if the user is determined to be in a state of "fatigue," the handover document will be adjusted to highlight important points and postpone tasks that require attention.

[0323] The handover document displayed on the terminal can be reviewed and modified by the user. After the user makes a final confirmation, the server sends the revised document to the next person in charge. Based on this information, the next person in charge can start their work quickly and smoothly.

[0324] As a concrete example, in tasks requiring project management, the server retrieves task progress data from a scheduling tool. If the emotion engine assesses the user's stress level as high, the generated handover document is simplified, and the priority of critical tasks is further emphasized. An example of a prompt to the generating AI model would be: "Analyze the user's work log and generate a handover document that takes into account the progress and emotional state of key tasks. If the user's current stress level is high, readjust the priorities."

[0325] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0326] Step 1:

[0327] The server establishes access to the information processing device using the user's authentication information. This access includes securely logging into various tools using API keys or the OAuth protocol. User authentication information is required as input, and the output is a state where access to the various tools is permitted.

[0328] Step 2:

[0329] The server collects business-related records from information processing devices with established access. Specifically, the server retrieves emails, schedule information, chat history, etc., via APIs. The input for this step is having access rights, and the output is the various business-related data that has been collected.

[0330] Step 3:

[0331] The server inputs the collected data into a generative AI model, which then analyzes the business processes. Here, the generative AI model uses natural language processing techniques to extract task progress and key information. A prompt is given to the model, and the analyzed business information is generated as output. Specifically, a prompt such as "Please extract task progress and key information" is input.

[0332] Step 4:

[0333] The server uses an emotion engine to analyze the user's input history and responses, and evaluates their emotional state. Specific operations include text tone analysis and biometric data analysis. The input for this step is user behavior data, and the output is the analyzed emotional state.

[0334] Step 5:

[0335] The server automatically generates handover documents based on analyzed business information and emotional states. The server integrates this data and combines the information in a way that is optimal for the user. The input to this process is business information and emotional states, and the output is the handover document.

[0336] Step 6:

[0337] The handover document is displayed on the terminal, and the user reviews its contents and makes any necessary corrections. Specifically, the user can modify the document's content using an editor. The input is an automatically generated handover document, and the output is the document reviewed and modified by the user.

[0338] Step 7:

[0339] Once the user has finished reviewing the document, the server sends the final version of the handover document to the next person in charge. This transmission is done via email or in-system messaging. The input is the reviewed final version of the document, and the output is the status of successful transmission.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0342] Current business handover processes are inefficient due to a lack of information organization and emotional support. In particular, the failure to optimize the handover process while considering the user's emotions is a major cause of stress and work inefficiency. Therefore, there is a need for a system that enables smooth business handover while taking the user's emotional state into account.

[0343] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0344] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems; means for collecting records related to the work from the information processing systems; means for analyzing the collected records and generating a generation model for generating related work information; and means for using an emotion analysis engine to estimate the user's emotional state and reflect it in the handover document. This enables a more efficient and smoother handover of work that takes into account the user's emotions.

[0345] A "user" refers to an individual or group that uses the system to perform their work.

[0346] "Authentication information" refers to the identification information that a user uses to access a system.

[0347] An "information processing system" refers to a system that includes tools and applications for processing, storing, or transferring data.

[0348] "Records" refer to data that includes the history of activities and transactions related to business operations.

[0349] "Generative model means" refers to a mechanism that includes technologies and algorithms for analyzing collected data and generating business information.

[0350] An "emotion analysis engine" refers to a program or technology that infers a user's emotional state and reflects the analysis results in the system.

[0351] A "handover document" refers to a document created to communicate job duties, procedures, and important information to a successor.

[0352] To implement this invention, two main elements are important: a server and a user terminal. The server receives authentication information from the user and establishes access to multiple information processing systems. It then automatically collects business-related records from these information processing systems. The collected records are analyzed by a generative modeling means running on the server, and relevant business information is generated.

[0353] The analysis utilizes a generative AI model. This model incorporates algorithms to integrate data and determine the priority of tasks relevant to the work. The server also features an emotion analysis engine, which uses data from user input and responses to infer the user's emotional state. The results of this analysis are reflected in the handover documents.

[0354] Specifically, the handover document displayed on the user's device includes not only the generated work information but also adjustments based on the user's emotional state. For example, if user fatigue is detected, the priority and explanation of important tasks are automatically modified. Furthermore, the user can review the handover document on their device and make corrections as needed.

[0355] In terms of hardware, smartphones and computers will be used for data collection and display. The software will include the generative AI model "GPT-3" and an "emotion analysis engine." A cloud platform that allows these software components to run efficiently is also desirable.

[0356] As a concrete example, a factory manager uses a smartphone to record the status of the production line, and a server collects and analyzes this data to generate a proposal for the tired manager to postpone high-load tasks. In this case, the AI ​​model that generates the proposal might use the following prompt: "Generate an optimized procedure for handing over production line check tasks when the user is tired."

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server receives authentication information from the user and establishes access to the information processing system. The input here is the user's authentication information, and the output is the connection status to the system. This creates a state where the necessary data can be collected based on the user's access rights.

[0360] Step 2:

[0361] The server collects business-related records from information processing systems. The input is log data obtained from each system, and the output is a dataset of the collected records. The server retrieves the data via an API and stores it in a database for centralized management.

[0362] Step 3:

[0363] The server analyzes the collected data using a generative AI model and generates relevant business information. The input is the collected log data, and the output is the analyzed business information. Generative AI models such as "GPT-3" are used for the analysis, and task progress and priorities are extracted through prompt messages.

[0364] Step 4:

[0365] The server uses an emotion analysis engine to estimate the user's emotional state. The input is the user's responses and input data, and the output is the analysis result of the user's emotional state. The emotion engine utilizes biometric data to quantify the user's stress and fatigue.

[0366] Step 5:

[0367] The server automatically generates handover documents based on the generated business information and emotional state. The input is analysis results and emotional data, and the output is a customized handover document. The server generates the document while reflecting the business information and highlighting important items.

[0368] Step 6:

[0369] The terminal presents the generated handover document to the user, allowing for review and modification. The input here is the generated handover document, and the output is the document modified by the user. The user can view the document on the terminal and make any necessary corrections.

[0370] Step 7:

[0371] The server sends the revised handover document to the next person in charge. This input is the final, confirmed handover document, and the output is ready to be sent to the next person in charge. Email and cloud platforms are used to facilitate a smooth handover of duties.

[0372] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0373] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0374] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0378] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0379] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0380] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0381] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0382] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0383] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0384] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0385] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0386] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0387] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0388] The system of this invention aims to streamline the handover process by collecting work logs from multiple tools that users use on a daily basis, then analyzing them using a generation AI model, and automatically generating handover documents.

[0389] The server accesses the tool's API based on the authentication information provided by the user and collects the necessary business logs from each tool. This eliminates the need for users to manually collect information directly from the tools and ensures that all data is collected without omission.

[0390] The acquired work logs are stored in a database on the server, organized, and then analyzed by a generating AI model. Based on the log content, the AI ​​model extracts information related to the work (task progress, important matters, relevant parties, etc.) and organizes the priority and relationships of each task.

[0391] The analyzed information is automatically generated by the server as a formatted handover document. This handover document is presented to the user on their terminal. The user can review this presented handover document and modify its contents as needed. The modified handover document is then checked again by the server and automatically distributed to the successor via email or other means.

[0392] For example, if a user uses Gmail to send many emails related to project negotiations, the server retrieves the email content and associated calendar information, and the AI ​​model analyzes it to produce results such as "Important Announcements for Project Y." This information is automatically recorded in the handover document as "Contact Person for Project Y: Mr. A, Next Deadline: October 20th."

[0393] In this way, the present invention aims to improve the overall operational efficiency of the organization by significantly reducing the effort and errors involved in handing over tasks, thereby effectively utilizing the labor of employees.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user logs into the system and enters authentication credentials to grant access to various tools. The server uses these credentials to establish a session to access APIs for Gmail, Calendar, chat tools, video conferencing tools, spreadsheet software, and presentation software.

[0397] Step 2:

[0398] The server periodically collects work logs related to the user's daily tasks from various tools. This collection includes email sending and receiving history, calendar schedules, chat conversation history, video conference participation records, and spreadsheet data update history. The collected data is stored in the server's database.

[0399] Step 3:

[0400] The server inputs the collected work logs into a generating AI model, which then performs an analysis of the work content. The generating AI model determines the priority, relevance, and progress of tasks from the log data and extracts relevant work information. For example, it identifies tasks belonging to a specific project, key contacts, and incomplete tasks.

[0401] Step 4:

[0402] The server automatically generates a draft of the handover document based on the analysis results. This handover document is formatted to include essential elements such as important work details, deadlines, information on the people involved, and the next tasks to be performed.

[0403] Step 5:

[0404] A draft of the handover document is displayed on the device, and the user reviews its contents. The user can check the accuracy of the contents and enter any necessary corrections or additions. Once corrections are complete, the user presses the "Confirm Handover Document" button.

[0405] Step 6:

[0406] The server sends the handover document, which has been revised and finalized by the user, to the successor. This is usually done via email or other means. The successor receives the finalized handover document on their device, allowing them to immediately access the information necessary to start their duties.

[0407] (Example 1)

[0408] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0409] Because fragments of information are scattered across various information processing devices, there are challenges such as important information being lost or integration being difficult during work handovers. This hinders efficient work handovers between employees and causes problems with work continuity.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing devices, means for collecting business-related records from the information processing devices, and means for generating a model for analyzing the collected records and generating relevant business information. This enables the integration of information and the unified management of important business information.

[0412] "Authentication information" refers to identification information required when a user accesses an information processing device, and includes, for example, a username, password, and authorization token.

[0413] An "information processing device" refers to a system or application for processing and managing digital data, and is used for communication and information management purposes.

[0414] "Records" refer to a collection of data that shows the process and results of work, including emails, meeting minutes, and task management information.

[0415] A "generative model" refers to an algorithm or artificial intelligence that analyzes collected data and automatically generates information aligned with a specific purpose.

[0416] A "report" is a document automatically generated based on the information that has been collected, and its purpose is to facilitate the handover of tasks and information sharing.

[0417] The "onboarding process" refers to the series of training and procedures required when a new person joins an organization or project.

[0418] The system of this invention is designed to streamline the handover process for business operations. The server establishes access to the API of an information processing device (e.g., an email system or a calendar management system) by receiving authentication information provided by the user. Google Workspace and Microsoft 365 are examples of such devices. The server collects the necessary records from these information processing devices and reliably gathers data, thus eliminating the effort required for manual data collection.

[0419] The acquired records are stored in a database on the server and organized appropriately. The server uses a generative AI model to analyze the stored records and extract information relevant to the work. The generative AI model analyzes the progress of tasks and relevant important matters from each record and automatically generates reports based on the extracted information.

[0420] For example, if a user is conducting important communication within a project, the server uses the Gmail API to collect relevant emails and retrieves meeting information from Google Calendar. The generative AI model then extracts "important communications regarding Project X" from these records and formats them into a report.

[0421] Users can review reports displayed on their terminals and make corrections as needed. The corrected reports are then reviewed by the server and automatically distributed to their successors via email or other means. This significantly reduces the effort and errors involved in handing over tasks, improving overall organizational efficiency.

[0422] An example of a prompt for a generating AI model is, "Extract important information related to Project X from Gmail and Calendar, and create a report." Based on this prompt, the AI ​​model analyzes the business information and integrates the necessary information into a report.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The server receives authentication information from the user. Based on this input information, the server establishes access to the APIs of each information processing device. Specifically, it uses the username and password entered by the user on the terminal to obtain an authentication token for the Google Workspace API and establishes a connection to the server. This ensures the communication channel necessary for subsequent data collection processes.

[0426] Step 2:

[0427] The server collects business-related records from information processing devices through established APIs. At this stage, email content and calendar events are retrieved as input data. The server retrieves business-related emails via the Gmail API and further collects data on related meetings and events using the Google Calendar API. This allows integrated business data to be stored in the server's database.

[0428] Step 3:

[0429] The server stores the collected records in a database and prepares to input the data into the generating AI model. This input work record is then analyzed by the AI ​​model. The server sends an analysis request to the generating AI model using pre-configured prompts, such as "Extract important tasks related to Project X from meeting information and email history." The AI ​​model then extracts and returns the task progress and other relevant important information.

[0430] Step 4:

[0431] Based on the analysis results obtained from the generated AI model, the server automatically generates a report. This generated report outputs the importance and progress of each task in an organized manner. The server receives the results and creates a report based on a format such as "Project X: Assignee, Deadline, Key Points." This makes it easy for the user to review the contents.

[0432] Step 5:

[0433] On the terminal, the user can review the generated report and make corrections as needed. The input is the user's edits to the report, allowing them to directly adjust the report's text on the terminal. The user then sends the final, revised report to the server as output, preparing it for distribution to their successor.

[0434] Step 6:

[0435] The server reviews the report corrected by the user and automatically sends it to the successor. This final output is a PDF document sent to the successor's mailbox. The server uses the default email service to email the revised report to the successor's address. This process ensures a smooth handover and maintains continuity.

[0436] (Application Example 1)

[0437] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] In manufacturing environments, the handover of tasks during shift changes is often inefficient. Insufficient information sharing leads to unclear progress tracking, hindering efficient work. Furthermore, the time-consuming process of verifying and correcting information places a heavy burden on workers. Therefore, there is a need to automate the handover process in factories and provide a means for rapid and accurate information sharing.

[0439] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0440] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems, means for collecting log data related to business operations from the information processing systems, and means for generating a generative artificial intelligence model that analyzes the collected log data and generates relevant business information. This makes it possible to automate the handover of tasks and effectively share information even when work shifts change in a manufacturing system.

[0441] A "user" refers to a worker who accesses an information system and takes over tasks.

[0442] "Authentication information" refers to the information a user needs to log in to an information processing system, and typically includes a username and password.

[0443] An "information processing system" is a computer system used for inputting, processing, and storing data related to business operations.

[0444] "Log data" refers to data that records the operation history of an information processing system and the progress of business operations.

[0445] A "generative artificial intelligence model" is an artificial intelligence technology that analyzes collected data and extracts and generates information necessary for business operations.

[0446] A "handover document" is a document that details the tasks and their progress, and its purpose is to enable a successor to quickly take over the responsibilities.

[0447] An "electronic information terminal" is an electronic device capable of displaying and inputting information, and refers to portable devices such as smartphones and tablets.

[0448] A "manufacturing system" is a set of machinery, equipment, and management systems used to produce a product.

[0449] This invention is implemented as a system to streamline the handover of tasks in manufacturing facilities. This system primarily consists of a server, an electronic information terminal, and multiple information processing systems. The server receives authentication information from the user and establishes access to the information processing systems. Using this authentication information, the server automatically collects task-related log data from information processing systems such as manufacturing management systems and quality control systems. At this stage, human intervention can be minimized.

[0450] The server implements a generative artificial intelligence model to analyze collected log data. The purpose of the analysis is to extract business information and generate work handover documents. This generated data is presented to the user via an electronic information terminal. The user can review the work handover document on the electronic information terminal and make corrections as needed. The corrected information is then shared with the next work shift via the server.

[0451] As a concrete example, during the shift change from night to day on a manufacturing line, this system automatically incorporates information about the previous night's work progress and machine status into a handover document, allowing the next shift's workers to quickly begin their duties. The generating AI model utilizes the latest technologies, such as OpenAI, demonstrating extensive analytical capabilities. An example of a prompt message is, "Analyze the following logs to create a summary of the work performed: including machine A's operating hours, maintenance records, and parts inventory status." This ensures continuity of operations and improves work efficiency.

[0452] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0453] Step 1:

[0454] The server receives authentication information from the user and establishes access to the information processing system. It receives the authentication information as input and uses it to connect to various information processing systems via APIs. Once the connection is established, access to business-related data becomes possible.

[0455] Step 2:

[0456] The server collects log data related to business operations from connected information processing systems. It collects data such as operating hours and maintenance records from these systems as input, integrates this data, and stores it in a database. This log data collection prepares the basic data necessary for subsequent analysis.

[0457] Step 3:

[0458] The server inputs the collected log data into a generating AI model, which extracts and analyzes business information. In this process, the generating AI model analyzes the input data and outputs the progress of the work and important points as a result. This identifies the necessary business information, which is then used in the next step.

[0459] Step 4:

[0460] The server automatically generates a handover document based on the analyzed business information. It uses business information obtained from the AI ​​model as input and outputs the handover document according to a standardized format. This output is in a format that is easy for the next worker to understand.

[0461] Step 5:

[0462] The terminal displays the generated handover document to the user. The user can review the handover document on the terminal and make corrections as needed. The input is the automatically generated handover document, and the output is the document after the user has made corrections.

[0463] Step 6:

[0464] The server sends the user-revised handover document to the successor. It receives the revised handover document as input and forwards it to the successor's device via email or cloud storage. This process ensures a reliable and rapid continuity of work.

[0465] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0466] This invention is a system that streamlines the handover of tasks and incorporates an emotion engine to consider the user's emotions during the handover process. This system collects work logs from various tools that users use on a daily basis, analyzes them using a generated AI model, and then uses the emotion engine to support the optimization of the handover.

[0467] The server uses user authentication credentials to establish connections to multiple tools (such as email, scheduling, and communication tools) and automatically collects work-related log data from them. The collected data is analyzed by a generative AI model to provide a detailed analysis of the user's work. The analysis results include the progress of key tasks and important future considerations.

[0468] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The emotion engine uses user input and responses on the tool, and in some cases biometric data, to infer the user's emotions. The results of the emotion engine's analysis are reflected in the generation of the handover document, and the content is adjusted according to the user's psychological state. For example, if the emotion engine determines that the user's stress level is high, the handover document will be generated in a more concise form that emphasizes important points.

[0469] After the handover document is generated, it is displayed on the terminal. The user can review this handover document and make corrections as needed. Once the user has completed their corrections, the final version is confirmed, and the server sends this confirmed handover document to the successor. The transmission method is email or similar, and the successor can smoothly begin their duties based on the received handover document.

[0470] As a concrete example, in a task requiring project management, if the emotion engine on the terminal detects signs of "fatigue" from the user, the handover document will be updated to readjust the task priorities, with tasks requiring attention being postponed. In this way, the present invention realizes efficient task handover that takes into account the user's emotions.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user logs into the system and provides their authentication credentials. This allows the server to prepare to establish access to multiple business tools (email, calendar, chat, etc.).

[0474] Step 2:

[0475] The server uses established access rights to collect log data related to the user's work from each tool. This data includes email subjects, conversation history, and calendar events. The collected data is stored in a database.

[0476] Step 3:

[0477] The server inputs the collected work logs into an AI model for detailed analysis of the work content. This analysis extracts important information such as task priorities, progress, and relationships.

[0478] Step 4:

[0479] The server collects user emotional data and inputs it into the emotion engine. The emotion engine analyzes the tone of the user's input and their behavior patterns during work hours to identify the user's current emotional state.

[0480] Step 5:

[0481] The server integrates the analysis results of the generated AI model and the results of the emotion engine, and automatically generates a handover document with appropriate adjustments. The way information is presented and the level of detail are adjusted according to the user's emotional state.

[0482] Step 6:

[0483] The generated transfer document is displayed on the device, and the user reviews its contents. The user can make corrections to the transfer document as needed.

[0484] Step 7:

[0485] Once the user completes the revisions, the terminal notifies the server that the handover document has been reviewed. The server then sends the final version of the handover document to the successor. The successor can then begin their work based on the received handover document.

[0486] (Example 2)

[0487] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] For efficient job handover, it's crucial not only to communicate job details but also to consider the user's feelings and emotional state. However, conventional systems struggle to automatically create handover documents that reflect the user's emotions, ultimately hindering smooth job transitions. Furthermore, they often fail to adequately integrate data from different information processing devices or automatically adjust job priorities. Therefore, there is a need for a system that enables more efficient handover while also considering emotional aspects.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0490] In this invention, the server includes means for receiving authentication information from a user and establishing access to an information processing device, means for collecting business-related records from the information processing device, and means for automatically generating a handover document based on sentiment analysis results. This makes it possible to automatically create a handover document that reflects the user's sentiment while integrating data from different information processing devices.

[0491] "Authentication information" refers to information used to identify a user and grant them permission to access information processing equipment.

[0492] "Information processing equipment" refers to a set of tools that provide business-related data, including email, schedule management, and communication tools.

[0493] "Records" refer to business-related data and logs collected from information processing equipment.

[0494] "Generative model means" refers to a model within a system that analyzes collected records and generates business information and task priorities.

[0495] A "handover document" is a document automatically generated by a generative model to communicate the details of the work to the next person in charge.

[0496] "Emotional analysis results" refer to the results of analyzing user input data and responses to evaluate the user's emotional state.

[0497] "Next person in charge" refers to the person who takes over the duties and becomes responsible for those duties.

[0498] This invention is a system for facilitating smooth business handover. It collects business-related records from multiple tools and analyzes them using a generative AI model. Based on the analysis results and user sentiment analysis results, it generates an optimized handover document. Specifically, the server uses user authentication information to access information processing devices such as email, schedule management, and communication tools. This allows the server to collect records necessary for the business and analyze that data in detail using the generative model.

[0499] The generative AI model used here analyzes collected data using natural language processing techniques to determine the importance and priority of related tasks. Furthermore, an emotion engine with sentiment analysis capabilities evaluates the user's emotional state, and the results are reflected in the content of the handover document. For example, if the user is determined to be in a state of "fatigue," the handover document will be adjusted to highlight important points and postpone tasks that require attention.

[0500] The handover document displayed on the terminal can be reviewed and modified by the user. After the user makes a final confirmation, the server sends the revised document to the next person in charge. Based on this information, the next person in charge can start their work quickly and smoothly.

[0501] As a concrete example, in tasks requiring project management, the server retrieves task progress data from a scheduling tool. If the emotion engine assesses the user's stress level as high, the generated handover document is simplified, and the priority of critical tasks is further emphasized. An example of a prompt to the generating AI model would be: "Analyze the user's work log and generate a handover document that takes into account the progress and emotional state of key tasks. If the user's current stress level is high, readjust the priorities."

[0502] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0503] Step 1:

[0504] The server establishes access to the information processing device using the user's authentication information. This access includes securely logging into various tools using API keys or the OAuth protocol. User authentication information is required as input, and the output is a state where access to the various tools is permitted.

[0505] Step 2:

[0506] The server collects business-related records from information processing devices with established access. Specifically, the server retrieves emails, schedule information, chat history, etc., via APIs. The input for this step is having access rights, and the output is the various business-related data that has been collected.

[0507] Step 3:

[0508] The server inputs the collected data into a generative AI model, which then analyzes the business processes. Here, the generative AI model uses natural language processing techniques to extract task progress and key information. A prompt is given to the model, and the analyzed business information is generated as output. Specifically, a prompt such as "Please extract task progress and key information" is input.

[0509] Step 4:

[0510] The server uses an emotion engine to analyze the user's input history and responses, and evaluates their emotional state. Specific operations include text tone analysis and biometric data analysis. The input for this step is user behavior data, and the output is the analyzed emotional state.

[0511] Step 5:

[0512] The server automatically generates handover documents based on analyzed business information and emotional states. The server integrates this data and combines the information in a way that is optimal for the user. The input to this process is business information and emotional states, and the output is the handover document.

[0513] Step 6:

[0514] The handover document is displayed on the terminal, and the user reviews its contents and makes any necessary corrections. Specifically, the user can modify the document's content using an editor. The input is an automatically generated handover document, and the output is the document reviewed and modified by the user.

[0515] Step 7:

[0516] Once the user has finished reviewing the document, the server sends the final version of the handover document to the next person in charge. This transmission is done via email or in-system messaging. The input is the reviewed final version of the document, and the output is the status of successful transmission.

[0517] (Application Example 2)

[0518] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Current business handover processes are inefficient due to a lack of information organization and emotional support. In particular, the failure to optimize the handover process while considering the user's emotions is a major cause of stress and work inefficiency. Therefore, there is a need for a system that enables smooth business handover while taking the user's emotional state into account.

[0520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0521] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems; means for collecting records related to the work from the information processing systems; means for analyzing the collected records and generating a generation model for generating related work information; and means for using an emotion analysis engine to estimate the user's emotional state and reflect it in the handover document. This enables a more efficient and smoother handover of work that takes into account the user's emotions.

[0522] A "user" refers to an individual or group that uses the system to perform their work.

[0523] "Authentication information" refers to the identification information that a user uses to access a system.

[0524] An "information processing system" refers to a system that includes tools and applications for processing, storing, or transferring data.

[0525] "Records" refer to data that includes the history of activities and transactions related to business operations.

[0526] "Generative model means" refers to a mechanism that includes technologies and algorithms for analyzing collected data and generating business information.

[0527] An "emotion analysis engine" refers to a program or technology that infers a user's emotional state and reflects the analysis results in the system.

[0528] A "handover document" refers to a document created to communicate job duties, procedures, and important information to a successor.

[0529] To implement this invention, two main elements are important: a server and a user terminal. The server receives authentication information from the user and establishes access to multiple information processing systems. It then automatically collects business-related records from these information processing systems. The collected records are analyzed by a generative modeling means running on the server, and relevant business information is generated.

[0530] The analysis utilizes a generative AI model. This model incorporates algorithms to integrate data and determine the priority of tasks relevant to the work. The server also features an emotion analysis engine, which uses data from user input and responses to infer the user's emotional state. The results of this analysis are reflected in the handover documents.

[0531] Specifically, the handover document displayed on the user's device includes not only the generated work information but also adjustments based on the user's emotional state. For example, if user fatigue is detected, the priority and explanation of important tasks are automatically modified. Furthermore, the user can review the handover document on their device and make corrections as needed.

[0532] In terms of hardware, smartphones and computers will be used for data collection and display. The software will include the generative AI model "GPT-3" and an "emotion analysis engine." A cloud platform that allows these software components to run efficiently is also desirable.

[0533] As a concrete example, a factory manager uses a smartphone to record the status of the production line, and a server collects and analyzes this data to generate a proposal for the tired manager to postpone high-load tasks. In this case, the AI ​​model that generates the proposal might use the following prompt: "Generate an optimized procedure for handing over production line check tasks when the user is tired."

[0534] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0535] Step 1:

[0536] The server receives authentication information from the user and establishes access to the information processing system. The input here is the user's authentication information, and the output is the connection status to the system. This creates a state where the necessary data can be collected based on the user's access rights.

[0537] Step 2:

[0538] The server collects business-related records from information processing systems. The input is log data obtained from each system, and the output is a dataset of the collected records. The server retrieves the data via an API and stores it in a database for centralized management.

[0539] Step 3:

[0540] The server analyzes the collected data using a generative AI model and generates relevant business information. The input is the collected log data, and the output is the analyzed business information. Generative AI models such as "GPT-3" are used for the analysis, and task progress and priorities are extracted through prompt messages.

[0541] Step 4:

[0542] The server uses an emotion analysis engine to estimate the user's emotional state. The input is the user's responses and input data, and the output is the analysis result of the user's emotional state. The emotion engine utilizes biometric data to quantify the user's stress and fatigue.

[0543] Step 5:

[0544] The server automatically generates handover documents based on the generated business information and emotional state. The input is analysis results and emotional data, and the output is a customized handover document. The server generates the document while reflecting the business information and highlighting important items.

[0545] Step 6:

[0546] The terminal presents the generated handover document to the user, allowing for review and modification. The input here is the generated handover document, and the output is the document modified by the user. The user can view the document on the terminal and make any necessary corrections.

[0547] Step 7:

[0548] The server sends the revised handover document to the next person in charge. This input is the final, confirmed handover document, and the output is ready to be sent to the next person in charge. Email and cloud platforms are used to facilitate a smooth handover of duties.

[0549] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0550] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0555] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0557] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0559] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0560] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0561] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0562] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0563] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0564] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0565] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0566] The system of this invention aims to streamline the handover process by collecting work logs from multiple tools that users use on a daily basis, then analyzing them using a generation AI model, and automatically generating handover documents.

[0567] The server accesses the tool's API based on the authentication information provided by the user and collects the necessary business logs from each tool. This eliminates the need for users to manually collect information directly from the tools and ensures that all data is collected without omission.

[0568] The acquired work logs are stored in a database on the server, organized, and then analyzed by a generating AI model. Based on the log content, the AI ​​model extracts information related to the work (task progress, important matters, relevant parties, etc.) and organizes the priority and relationships of each task.

[0569] The analyzed information is automatically generated by the server as a formatted handover document. This handover document is presented to the user on their terminal. The user can review this presented handover document and modify its contents as needed. The modified handover document is then checked again by the server and automatically distributed to the successor via email or other means.

[0570] For example, if a user uses Gmail to send many emails related to project negotiations, the server retrieves the email content and associated calendar information, and the AI ​​model analyzes it to produce results such as "Important Announcements for Project Y." This information is automatically recorded in the handover document as "Contact Person for Project Y: Mr. A, Next Deadline: October 20th."

[0571] In this way, the present invention aims to improve the overall operational efficiency of the organization by significantly reducing the effort and errors involved in handing over tasks, thereby effectively utilizing the labor of employees.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The user logs into the system and enters authentication credentials to grant access to various tools. The server uses these credentials to establish a session to access APIs for Gmail, Calendar, chat tools, video conferencing tools, spreadsheet software, and presentation software.

[0575] Step 2:

[0576] The server periodically collects work logs related to the user's daily tasks from various tools. This collection includes email sending and receiving history, calendar schedules, chat conversation history, video conference participation records, and spreadsheet data update history. The collected data is stored in the server's database.

[0577] Step 3:

[0578] The server inputs the collected work logs into a generating AI model, which then performs an analysis of the work content. The generating AI model determines the priority, relevance, and progress of tasks from the log data and extracts relevant work information. For example, it identifies tasks belonging to a specific project, key contacts, and incomplete tasks.

[0579] Step 4:

[0580] The server automatically generates a draft of the handover document based on the analysis results. This handover document is formatted to include essential elements such as important work details, deadlines, information on the people involved, and the next tasks to be performed.

[0581] Step 5:

[0582] A draft of the handover document is displayed on the device, and the user reviews its contents. The user can check the accuracy of the contents and enter any necessary corrections or additions. Once corrections are complete, the user presses the "Confirm Handover Document" button.

[0583] Step 6:

[0584] The server sends the handover document, which has been revised and finalized by the user, to the successor. This is usually done via email or other means. The successor receives the finalized handover document on their device, allowing them to immediately access the information necessary to start their duties.

[0585] (Example 1)

[0586] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0587] Because fragments of information are scattered across various information processing devices, there are challenges such as important information being lost or integration being difficult during work handovers. This hinders efficient work handovers between employees and causes problems with work continuity.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0589] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing devices, means for collecting business-related records from the information processing devices, and means for generating a model for analyzing the collected records and generating relevant business information. This enables the integration of information and the unified management of important business information.

[0590] "Authentication information" refers to identification information required when a user accesses an information processing device, and includes, for example, a username, password, and authorization token.

[0591] An "information processing device" refers to a system or application for processing and managing digital data, and is used for communication and information management purposes.

[0592] "Records" refer to a collection of data that shows the process and results of work, including emails, meeting minutes, and task management information.

[0593] A "generative model" refers to an algorithm or artificial intelligence that analyzes collected data and automatically generates information aligned with a specific purpose.

[0594] A "report" is a document automatically generated based on the information that has been collected, and its purpose is to facilitate the handover of tasks and information sharing.

[0595] The "onboarding process" refers to the series of training and procedures required when a new person joins an organization or project.

[0596] The system of this invention is designed to streamline the handover process for business operations. The server establishes access to the API of an information processing device (e.g., an email system or a calendar management system) by receiving authentication information provided by the user. Google Workspace and Microsoft 365 are examples of such devices. The server collects the necessary records from these information processing devices and reliably gathers data, thus eliminating the effort required for manual data collection.

[0597] The acquired records are stored in a database on the server and organized appropriately. The server uses a generative AI model to analyze the stored records and extract information relevant to the work. The generative AI model analyzes the progress of tasks and relevant important matters from each record and automatically generates reports based on the extracted information.

[0598] For example, if a user is conducting important communication within a project, the server uses the Gmail API to collect relevant emails and retrieves meeting information from Google Calendar. The generative AI model then extracts "important communications regarding Project X" from these records and formats them into a report.

[0599] Users can review reports displayed on their terminals and make corrections as needed. The corrected reports are then reviewed by the server and automatically distributed to their successors via email or other means. This significantly reduces the effort and errors involved in handing over tasks, improving overall organizational efficiency.

[0600] An example of a prompt for a generating AI model is, "Extract important information related to Project X from Gmail and Calendar, and create a report." Based on this prompt, the AI ​​model analyzes the business information and integrates the necessary information into a report.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] The server receives authentication information from the user. Based on this input information, the server establishes access to the APIs of each information processing device. Specifically, it uses the username and password entered by the user on the terminal to obtain an authentication token for the Google Workspace API and establishes a connection to the server. This ensures the communication channel necessary for subsequent data collection processes.

[0604] Step 2:

[0605] The server collects business-related records from information processing devices through established APIs. At this stage, email content and calendar events are retrieved as input data. The server retrieves business-related emails via the Gmail API and further collects data on related meetings and events using the Google Calendar API. This allows integrated business data to be stored in the server's database.

[0606] Step 3:

[0607] The server stores the collected records in a database and prepares to input the data into the generating AI model. This input work record is then analyzed by the AI ​​model. The server sends an analysis request to the generating AI model using pre-configured prompts, such as "Extract important tasks related to Project X from meeting information and email history." The AI ​​model then extracts and returns the task progress and other relevant important information.

[0608] Step 4:

[0609] Based on the analysis results obtained from the generated AI model, the server automatically generates a report. This generated report outputs the importance and progress of each task in an organized manner. The server receives the results and creates a report based on a format such as "Project X: Assignee, Deadline, Key Points." This makes it easy for the user to review the contents.

[0610] Step 5:

[0611] On the terminal, the user can review the generated report and make corrections as needed. The input is the user's edits to the report, allowing them to directly adjust the report's text on the terminal. The user then sends the final, revised report to the server as output, preparing it for distribution to their successor.

[0612] Step 6:

[0613] The server reviews the report corrected by the user and automatically sends it to the successor. This final output is a PDF document sent to the successor's mailbox. The server uses the default email service to email the revised report to the successor's address. This process ensures a smooth handover and maintains continuity.

[0614] (Application Example 1)

[0615] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] In manufacturing environments, the handover of tasks during shift changes is often inefficient. Insufficient information sharing leads to unclear progress tracking, hindering efficient work. Furthermore, the time-consuming process of verifying and correcting information places a heavy burden on workers. Therefore, there is a need to automate the handover process in factories and provide a means for rapid and accurate information sharing.

[0617] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0618] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems, means for collecting log data related to business operations from the information processing systems, and means for generating a generative artificial intelligence model that analyzes the collected log data and generates relevant business information. This makes it possible to automate the handover of tasks and effectively share information even when work shifts change in a manufacturing system.

[0619] A "user" refers to a worker who accesses an information system and takes over tasks.

[0620] "Authentication information" refers to the information a user needs to log in to an information processing system, and typically includes a username and password.

[0621] An "information processing system" is a computer system used for inputting, processing, and storing data related to business operations.

[0622] "Log data" refers to data that records the operation history of an information processing system and the progress of business operations.

[0623] A "generative artificial intelligence model" is an artificial intelligence technology that analyzes collected data and extracts and generates information necessary for business operations.

[0624] A "handover document" is a document that details the tasks and their progress, and its purpose is to enable a successor to quickly take over the responsibilities.

[0625] An "electronic information terminal" is an electronic device capable of displaying and inputting information, and refers to portable devices such as smartphones and tablets.

[0626] A "manufacturing system" is a set of machinery, equipment, and management systems used to produce a product.

[0627] This invention is implemented as a system to streamline the handover of tasks in manufacturing facilities. This system primarily consists of a server, an electronic information terminal, and multiple information processing systems. The server receives authentication information from the user and establishes access to the information processing systems. Using this authentication information, the server automatically collects task-related log data from information processing systems such as manufacturing management systems and quality control systems. At this stage, human intervention can be minimized.

[0628] The server implements a generative artificial intelligence model to analyze collected log data. The purpose of the analysis is to extract business information and generate work handover documents. This generated data is presented to the user via an electronic information terminal. The user can review the work handover document on the electronic information terminal and make corrections as needed. The corrected information is then shared with the next work shift via the server.

[0629] As a concrete example, during the shift change from night to day on a manufacturing line, this system automatically incorporates information about the previous night's work progress and machine status into a handover document, allowing the next shift's workers to quickly begin their duties. The generating AI model utilizes the latest technologies, such as OpenAI, demonstrating extensive analytical capabilities. An example of a prompt message is, "Analyze the following logs to create a summary of the work performed: including machine A's operating hours, maintenance records, and parts inventory status." This ensures continuity of operations and improves work efficiency.

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The server receives authentication information from the user and establishes access to the information processing system. It receives the authentication information as input and uses it to connect to various information processing systems via APIs. Once the connection is established, access to business-related data becomes possible.

[0633] Step 2:

[0634] The server collects log data related to business operations from connected information processing systems. It collects data such as operating hours and maintenance records from these systems as input, integrates this data, and stores it in a database. This log data collection prepares the basic data necessary for subsequent analysis.

[0635] Step 3:

[0636] The server inputs the collected log data into a generating AI model, which extracts and analyzes business information. In this process, the generating AI model analyzes the input data and outputs the progress of the work and important points as a result. This identifies the necessary business information, which is then used in the next step.

[0637] Step 4:

[0638] The server automatically generates a handover document based on the analyzed business information. It uses business information obtained from the AI ​​model as input and outputs the handover document according to a standardized format. This output is in a format that is easy for the next worker to understand.

[0639] Step 5:

[0640] The terminal displays the generated handover document to the user. The user can review the handover document on the terminal and make corrections as needed. The input is the automatically generated handover document, and the output is the document after the user has made corrections.

[0641] Step 6:

[0642] The server sends the user-revised handover document to the successor. It receives the revised handover document as input and forwards it to the successor's device via email or cloud storage. This process ensures a reliable and rapid continuity of work.

[0643] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0644] This invention is a system that streamlines the handover of tasks and incorporates an emotion engine to consider the user's emotions during the handover process. This system collects work logs from various tools that users use on a daily basis, analyzes them using a generated AI model, and then uses the emotion engine to support the optimization of the handover.

[0645] The server uses user authentication credentials to establish connections to multiple tools (such as email, scheduling, and communication tools) and automatically collects work-related log data from them. The collected data is analyzed by a generative AI model to provide a detailed analysis of the user's work. The analysis results include the progress of key tasks and important future considerations.

[0646] Furthermore, this system incorporates an emotion engine that analyzes the user's emotional state. The emotion engine uses user input and responses on the tool, and in some cases biometric data, to infer the user's emotions. The results of the emotion engine's analysis are reflected in the generation of the handover document, and the content is adjusted according to the user's psychological state. For example, if the emotion engine determines that the user's stress level is high, the handover document will be generated in a more concise form that emphasizes important points.

[0647] After the handover document is generated, it is displayed on the terminal. The user can review this handover document and make corrections as needed. Once the user has completed their corrections, the final version is confirmed, and the server sends this confirmed handover document to the successor. The transmission method is email or similar, and the successor can smoothly begin their duties based on the received handover document.

[0648] As a concrete example, in a task requiring project management, if the emotion engine on the terminal detects signs of "fatigue" from the user, the handover document will be updated to readjust the task priorities, with tasks requiring attention being postponed. In this way, the present invention realizes efficient task handover that takes into account the user's emotions.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The user logs into the system and provides their authentication credentials. This allows the server to prepare to establish access to multiple business tools (email, calendar, chat, etc.).

[0652] Step 2:

[0653] The server uses established access rights to collect log data related to the user's work from each tool. This data includes email subjects, conversation history, and calendar events. The collected data is stored in a database.

[0654] Step 3:

[0655] The server inputs the collected work logs into an AI model for detailed analysis of the work content. This analysis extracts important information such as task priorities, progress, and relationships.

[0656] Step 4:

[0657] The server collects user emotional data and inputs it into the emotion engine. The emotion engine analyzes the tone of the user's input and their behavior patterns during work hours to identify the user's current emotional state.

[0658] Step 5:

[0659] The server integrates the analysis results of the generated AI model and the results of the emotion engine, and automatically generates a handover document with appropriate adjustments. The way information is presented and the level of detail are adjusted according to the user's emotional state.

[0660] Step 6:

[0661] The generated transfer document is displayed on the device, and the user reviews its contents. The user can make corrections to the transfer document as needed.

[0662] Step 7:

[0663] Once the user completes the revisions, the terminal notifies the server that the handover document has been reviewed. The server then sends the final version of the handover document to the successor. The successor can then begin their work based on the received handover document.

[0664] (Example 2)

[0665] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] For efficient job handover, it's crucial not only to communicate job details but also to consider the user's feelings and emotional state. However, conventional systems struggle to automatically create handover documents that reflect the user's emotions, ultimately hindering smooth job transitions. Furthermore, they often fail to adequately integrate data from different information processing devices or automatically adjust job priorities. Therefore, there is a need for a system that enables more efficient handover while also considering emotional aspects.

[0667] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0668] In this invention, the server includes means for receiving authentication information from a user and establishing access to an information processing device, means for collecting business-related records from the information processing device, and means for automatically generating a handover document based on sentiment analysis results. This makes it possible to automatically create a handover document that reflects the user's sentiment while integrating data from different information processing devices.

[0669] "Authentication information" refers to information used to identify a user and grant them permission to access information processing equipment.

[0670] "Information processing equipment" refers to a set of tools that provide business-related data, including email, schedule management, and communication tools.

[0671] "Records" refer to business-related data and logs collected from information processing equipment.

[0672] "Generative model means" refers to a model within a system that analyzes collected records and generates business information and task priorities.

[0673] A "handover document" is a document automatically generated by a generative model to communicate the details of the work to the next person in charge.

[0674] "Emotional analysis results" refer to the results of analyzing user input data and responses to evaluate the user's emotional state.

[0675] "Next person in charge" refers to the person who takes over the duties and becomes responsible for those duties.

[0676] This invention is a system for facilitating smooth business handover. It collects business-related records from multiple tools and analyzes them using a generative AI model. Based on the analysis results and user sentiment analysis results, it generates an optimized handover document. Specifically, the server uses user authentication information to access information processing devices such as email, schedule management, and communication tools. This allows the server to collect records necessary for the business and analyze that data in detail using the generative model.

[0677] The generative AI model used here analyzes collected data using natural language processing techniques to determine the importance and priority of related tasks. Furthermore, an emotion engine with sentiment analysis capabilities evaluates the user's emotional state, and the results are reflected in the content of the handover document. For example, if the user is determined to be in a state of "fatigue," the handover document will be adjusted to highlight important points and postpone tasks that require attention.

[0678] The handover document displayed on the terminal can be reviewed and modified by the user. After the user makes a final confirmation, the server sends the revised document to the next person in charge. Based on this information, the next person in charge can start their work quickly and smoothly.

[0679] As a concrete example, in tasks requiring project management, the server retrieves task progress data from a scheduling tool. If the emotion engine assesses the user's stress level as high, the generated handover document is simplified, and the priority of critical tasks is further emphasized. An example of a prompt to the generating AI model would be: "Analyze the user's work log and generate a handover document that takes into account the progress and emotional state of key tasks. If the user's current stress level is high, readjust the priorities."

[0680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0681] Step 1:

[0682] The server establishes access to the information processing device using the user's authentication information. This access includes securely logging into various tools using API keys or the OAuth protocol. User authentication information is required as input, and the output is a state where access to the various tools is permitted.

[0683] Step 2:

[0684] The server collects business-related records from information processing devices with established access. Specifically, the server retrieves emails, schedule information, chat history, etc., via APIs. The input for this step is having access rights, and the output is the various business-related data that has been collected.

[0685] Step 3:

[0686] The server inputs the collected data into a generative AI model, which then analyzes the business processes. Here, the generative AI model uses natural language processing techniques to extract task progress and key information. A prompt is given to the model, and the analyzed business information is generated as output. Specifically, a prompt such as "Please extract task progress and key information" is input.

[0687] Step 4:

[0688] The server uses an emotion engine to analyze the user's input history and responses, and evaluates their emotional state. Specific operations include text tone analysis and biometric data analysis. The input for this step is user behavior data, and the output is the analyzed emotional state.

[0689] Step 5:

[0690] The server automatically generates handover documents based on analyzed business information and emotional states. The server integrates this data and combines the information in a way that is optimal for the user. The input to this process is business information and emotional states, and the output is the handover document.

[0691] Step 6:

[0692] The handover document is displayed on the terminal, and the user reviews its contents and makes any necessary corrections. Specifically, the user can modify the document's content using an editor. The input is an automatically generated handover document, and the output is the document reviewed and modified by the user.

[0693] Step 7:

[0694] Once the user has finished reviewing the document, the server sends the final version of the handover document to the next person in charge. This transmission is done via email or in-system messaging. The input is the reviewed final version of the document, and the output is the status of successful transmission.

[0695] (Application Example 2)

[0696] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0697] Current business handover processes are inefficient due to a lack of information organization and emotional support. In particular, the failure to optimize the handover process while considering the user's emotions is a major cause of stress and work inefficiency. Therefore, there is a need for a system that enables smooth business handover while taking the user's emotional state into account.

[0698] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0699] In this invention, the server includes means for receiving authentication information from a user and establishing access to multiple information processing systems; means for collecting records related to the work from the information processing systems; means for analyzing the collected records and generating a generation model for generating related work information; and means for using an emotion analysis engine to estimate the user's emotional state and reflect it in the handover document. This enables a more efficient and smoother handover of work that takes into account the user's emotions.

[0700] A "user" refers to an individual or group that uses the system to perform their work.

[0701] "Authentication information" refers to the identification information that a user uses to access a system.

[0702] An "information processing system" refers to a system that includes tools and applications for processing, storing, or transferring data.

[0703] "Records" refer to data that includes the history of activities and transactions related to business operations.

[0704] "Generative model means" refers to a mechanism that includes technologies and algorithms for analyzing collected data and generating business information.

[0705] An "emotion analysis engine" refers to a program or technology that infers a user's emotional state and reflects the analysis results in the system.

[0706] A "handover document" refers to a document created to communicate job duties, procedures, and important information to a successor.

[0707] To implement this invention, two main elements are important: a server and a user terminal. The server receives authentication information from the user and establishes access to multiple information processing systems. It then automatically collects business-related records from these information processing systems. The collected records are analyzed by a generative modeling means running on the server, and relevant business information is generated.

[0708] The analysis utilizes a generative AI model. This model incorporates algorithms to integrate data and determine the priority of tasks relevant to the work. The server also features an emotion analysis engine, which uses data from user input and responses to infer the user's emotional state. The results of this analysis are reflected in the handover documents.

[0709] Specifically, the handover document displayed on the user's device includes not only the generated work information but also adjustments based on the user's emotional state. For example, if user fatigue is detected, the priority and explanation of important tasks are automatically modified. Furthermore, the user can review the handover document on their device and make corrections as needed.

[0710] In terms of hardware, smartphones and computers will be used for data collection and display. The software will include the generative AI model "GPT-3" and an "emotion analysis engine." A cloud platform that allows these software components to run efficiently is also desirable.

[0711] As a concrete example, a factory manager uses a smartphone to record the status of the production line, and a server collects and analyzes this data to generate a proposal for the tired manager to postpone high-load tasks. In this case, the AI ​​model that generates the proposal might use the following prompt: "Generate an optimized procedure for handing over production line check tasks when the user is tired."

[0712] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0713] Step 1:

[0714] The server receives authentication information from the user and establishes access to the information processing system. The input here is the user's authentication information, and the output is the connection status to the system. This creates a state where the necessary data can be collected based on the user's access rights.

[0715] Step 2:

[0716] The server collects business-related records from information processing systems. The input is log data obtained from each system, and the output is a dataset of the collected records. The server retrieves the data via an API and stores it in a database for centralized management.

[0717] Step 3:

[0718] The server analyzes the collected data using a generative AI model and generates relevant business information. The input is the collected log data, and the output is the analyzed business information. Generative AI models such as "GPT-3" are used for the analysis, and task progress and priorities are extracted through prompt messages.

[0719] Step 4:

[0720] The server uses an emotion analysis engine to estimate the user's emotional state. The input is the user's responses and input data, and the output is the analysis result of the user's emotional state. The emotion engine utilizes biometric data to quantify the user's stress and fatigue.

[0721] Step 5:

[0722] The server automatically generates handover documents based on the generated business information and emotional state. The input is analysis results and emotional data, and the output is a customized handover document. The server generates the document while reflecting the business information and highlighting important items.

[0723] Step 6:

[0724] The terminal presents the generated handover document to the user, allowing for review and modification. The input here is the generated handover document, and the output is the document modified by the user. The user can view the document on the terminal and make any necessary corrections.

[0725] Step 7:

[0726] The server sends the revised handover document to the next person in charge. This input is the final, confirmed handover document, and the output is ready to be sent to the next person in charge. Email and cloud platforms are used to facilitate a smooth handover of duties.

[0727] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0728] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0730] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0731] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0732] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0733] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0734] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0735] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0736] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0737] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0738] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0739] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0740] 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.

[0741] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0742] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0743] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0744] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0745] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0746] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0747] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] A means of receiving authentication information from users and establishing access to multiple tools,

[0751] A means for collecting business-related logs from the aforementioned tool,

[0752] A generation model means that analyzes collected logs and generates relevant business information,

[0753] A means for automatically generating a handover document based on the business information generated by the aforementioned generation model means,

[0754] A means to present the user with a handover document and allow them to review and correct it,

[0755] A means of sending the revised handover document to the successor,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, wherein the generation model means integrates data from different tools and prioritizes tasks related to the business.

[0759] (Claim 3)

[0760] The system according to claim 1, further comprising means for using the modified handover document as part of the onboarding process.

[0761] "Example 1"

[0762] (Claim 1)

[0763] A means of receiving authentication information from a user and establishing access to multiple information processing devices,

[0764] A means for collecting business-related records from the aforementioned information processing device,

[0765] A generation model means that analyzes collected records and generates related business information,

[0766] A means for automatically generating a report based on the business information generated by the aforementioned generation model means,

[0767] A means of presenting reports to users and enabling them to review and correct them,

[0768] A means of sending the revised report to the successor,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, wherein the generation model means integrates data between different information processing devices and prioritizes tasks related to the business.

[0772] (Claim 3)

[0773] The system according to claim 1, further comprising means for using the aforementioned revised report as part of the implementation process.

[0774] "Application Example 1"

[0775] (Claim 1)

[0776] A means of receiving authentication information from a user and establishing access to multiple information processing systems,

[0777] A means for collecting log data related to business operations from the aforementioned information processing system,

[0778] A generative artificial intelligence model means that analyzes collected log data and generates relevant business information,

[0779] A means for automatically generating a business handover document based on business information generated by the aforementioned generative artificial intelligence model means,

[0780] A means to present the user with a handover document and allow them to review and revise it,

[0781] A means of sending the revised handover document to the successor,

[0782] A means of displaying a work handover document on an electronic information terminal when changing work shifts in a manufacturing system,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, wherein the generative artificial intelligence model means integrates data from different information processing systems and assigns importance to business tasks related to the business.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising means for using the modified handover document as part of the product manufacturing process.

[0788] "Example 2 of combining an emotion engine"

[0789] (Claim 1)

[0790] A means of receiving authentication information from a user and establishing access to a large-scale information processing device,

[0791] A means for collecting business-related records from the aforementioned information processing device,

[0792] A generation model means that analyzes collected records and generates related business information,

[0793] A means for automatically generating a handover document based on the business information and sentiment analysis results generated by the aforementioned generation model means,

[0794] A means of displaying the handover document on the terminal and enabling confirmation and modification,

[0795] A means of sending the revised handover document to the next person in charge,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the generation model means integrates data between different information processing devices and prioritizes tasks related to the business.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising means for adjusting the content of the handover document based on the recorded emotional state of the user, taking into consideration the results of the emotion analysis.

[0801] "Application example 2 of combining emotional engines"

[0802] (Claim 1)

[0803] A means of receiving authentication information from a user and establishing access to multiple information processing systems,

[0804] A means for collecting business-related records from the aforementioned information processing system,

[0805] A generation model means that analyzes collected records and generates related business information,

[0806] A means for automatically generating handover documents based on the business information generated by the aforementioned generation model means,

[0807] A means of presenting the handover document to the user and enabling them to review and modify it,

[0808] A means of sending the revised handover document to the next person in charge,

[0809] A means of using an emotion analysis engine to infer the user's emotional state and reflect it in the handover document,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the generation model means integrates data from different information processing systems and prioritizes tasks related to the business.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising means for using the aforementioned modified handover document as part of the new employee training process. [Explanation of Symbols]

[0815] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving authentication information from users and establishing access to multiple tools, A means for collecting business-related logs from the aforementioned tool, A generation model means that analyzes collected logs and generates relevant business information, A means for automatically generating a handover document based on the business information generated by the aforementioned generation model means, A means to present the user with a handover document and allow them to review and correct it, A means of sending the revised handover document to the successor, A system that includes this.

2. The system according to claim 1, wherein the generation model means integrates data from different tools and prioritizes tasks related to the business.

3. The system according to claim 1, further comprising means for using the modified handover document as part of the onboarding process.

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