system

Abstract

We provide the system. [Solution] A means for collecting user email data, A means of collecting user project management data, A means of collecting user document management data, A means of analyzing and summarizing each collected data using natural language processing, A means of organizing summarized data by category, A system that includes means for displaying organized data as a user interface.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional business handovers have been greatly influenced by human documentation capabilities and motivation, and have sometimes imposed a great burden on the recipient. In particular, there has been a problem that smooth continuation of business becomes difficult due to an excessive amount of handover documents or incomplete documents. In addition, when handing over between different departments, or when the person in charge transfers or leaves the company, the handover work becomes even more complicated and often requires time and labor. Solving such problems and improving the efficiency and continuity of business are the problems of the present invention.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information. The system according to the present invention includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each of the collected data using natural language processing, means for organizing the summarized data by category, and means for displaying the organized data as a user interface. Furthermore, it is equipped with means for user authentication to obtain permission for data access, and the collected data is acquired using an appropriate communication protocol to ensure the integrity and security of the information. This reduces the burden on the user and realizes efficient and smooth business handover.

[0006] "User" refers to an individual or legal entity that uses the system.

[0007] "Email data" refers to the content, metadata, and related information of emails sent and received by a user.

[0008] "Project management data" refers to tasks, progress, communication among team members, and related information that users handle on project management tools.

[0009] "Document management data" refers to documents, files, metadata, and related information that users have saved in a document management system.

[0010] "Means of collection" refers to the technical methods and processes used to acquire specific data and incorporate it into a system.

[0011] "Natural language processing" refers to the technology that enables computers to understand and process human language.

[0012] "Methods of summarization" refer to technical methods and processes for extracting key points from a large amount of information and summarizing them concisely.

[0013] "Methods for organizing by category" refers to technical methods and processes for classifying summarized data into specific categories and organizing them systematically.

[0014] "User interface" refers to the part of a system that includes screens and means of operation for the user to interact with the system.

[0015] "Authentication information" refers to information used to identify a user and verify their access rights.

[0016] A "communication protocol" refers to a set of standard rules that define the conventions and procedures for data communication. [Brief explanation of the drawing]

[0017] [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 the data processing device and 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] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Also, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information.

[0039] System Configuration

[0040] This system includes the following main components:

[0041] 1. Data acquisition methods

[0042] 2. Data summarization means

[0043] 3. Data organization methods

[0044] 4. Means of providing a user interface

[0045] Specific behavior of each component

[0046] Data acquisition methods

[0047] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[0048] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[0049] Data summarization means

[0050] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[0051] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[0052] Data organization methods

[0053] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0054] User interface provisioning means

[0055] The organized data is provided as a user interface on the device for user access. The device receives the organized data retrieved from the server and displays it as an intuitive dashboard. This allows users to easily check the transfer information.

[0056] Specific example

[0057] Example 1: Data Collection

[0058] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system.

[0059] Example 2: Data summarization and organization

[0060] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information such as the achievement status of important milestones in a particular project and the division of roles among key members.

[0061] Example 3: Provision to users

[0062] Finally, this summarized data is organized and presented as a user interface. User B can access a dashboard provided by the server via their terminal, allowing them to quickly grasp the email summary, project progress, and an overview of related documents.

[0063] As described above, the present invention automates information gathering, summarization, and organization during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[0067] Step 2:

[0068] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[0069] Step 3:

[0070] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[0071] Step 4:

[0072] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[0073] Step 5:

[0074] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[0075] Step 6:

[0076] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[0077] Step 7:

[0078] The server organizes the summarized data by category. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document."

[0079] Step 8:

[0080] The server formats the organized data into datasets for delivery as a user interface. It converts it into a format that users can easily access.

[0081] Step 9:

[0082] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[0083] Step 10:

[0084] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, which helps ensure the continuity of their work.

[0085] (Example 1)

[0086] 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."

[0087] Gathering, summarizing, and organizing information from numerous different data sources during a business handover is extremely time-consuming and laborious. Manually processing a wide range of data, particularly emails, project management, and document management, is cumbersome and prone to errors. Furthermore, there is a need for an effective method to present this information to users efficiently.

[0088] 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.

[0089] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for verifying authentication information and obtaining permission for user data access, means for specifying data categories and displaying the organized data as a user interface, and means for analyzing the collected data using a generation AI model and generating prompt sentences. This makes it possible to automate data collection, analysis, summarization, organization, and display during business handover, significantly reducing time and effort.

[0090] Definitions of important words

[0091] "Means of collecting user email data" refers to a function that retrieves email data from the email server used by the user via an API or similar means.

[0092] "Means for collecting user project management data" refers to a function that retrieves project data from the project management tool used by the user via APIs or other means.

[0093] "Means for collecting user document management data" refers to the function of obtaining document data from the document management system used by the user via APIs or other means.

[0094] "Means for analyzing and summarizing collected data using natural language processing" refers to a function that analyzes acquired data using machine learning and natural language processing techniques, extracts only the important information, and generates a summary.

[0095] "Means for organizing summarized data by category" refers to a function that classifies and organizes the generated summary text into categories such as emails, projects, and documents.

[0096] "Means of verifying authentication information and obtaining permission for user data access" refers to a function that verifies the user's authentication information, authenticates that they are a legitimate user, and then obtains the authority to access the data.

[0097] "A means of specifying data categories and displaying organized data as a user interface" refers to a function that displays organized data by category and provides it in a dashboard-style user interface that allows users to operate it intuitively.

[0098] "A means of analyzing collected data using a generative AI model and generating prompt sentences" refers to a function that analyzes collected data using an advanced AI model and generates the necessary prompt sentences.

[0099] "Means of using predetermined communication protocols" refers to a function that uses standardized protocols when sending and receiving data to ensure secure and efficient communication.

[0100] Modes for carrying out the invention

[0101] This invention is a system that automatically collects various business data from users, summarizes and organizes it using natural language processing (NLP) technology, and provides it to the user. The following describes how this system is specifically implemented.

[0102] System Configuration

[0103] This system includes the following main components.

[0104] 1. Data acquisition methods

[0105] 2. Data summarization means

[0106] 3. Data organization methods

[0107] 4. Means of providing a user interface

[0108] 5. Authentication Information Verification Method

[0109] 6. Analysis methods using generative AI models

[0110] Hardware and software to be used

[0111] The system uses the following hardware and software:

[0112] hardware

[0113] Server: The central hardware responsible for data collection, analysis, and organization.

[0114] Device: A device used by the user to view data (PC, tablet, smartphone, etc.)

[0115] software

[0116] Cloud infrastructure such as GCP (Google Cloud Platform) and AWS (Amazon Web Services)

[0117] Various data source APIs such as Gmail API, Trello API, and Google Drive API

[0118] Natural language processing models such as the BERT model

[0119] HTTP and HTTPS communication protocols

[0120] Data acquisition methods

[0121] The server first verifies the user's authentication information and obtains permission to access the data. Next, it collects data from each data source (e.g., email server, project management tool, document management system). For example, it can retrieve email data via the Gmail API and collect project data using the Trello or Asana APIs. It can also retrieve document data from Google Drive or Dropbox.

[0122] Data summarization means

[0123] The server analyzes the collected data using natural language processing techniques, extracting and summarizing important information. Generative AI models such as the BERT model are used for this process. For example, the following prompt statements are used:

[0124] "Please summarize the key points from your emails over the past six months."

[0125] Data organization methods

[0126] The summarized data is organized by category. For example, email summaries are categorized under "Emails," project progress data under "Projects," and document-related summaries under "Documents."

[0127] User interface provisioning means

[0128] The terminal receives organized data from the server and presents it as a user interface. Specifically, it is displayed in a dashboard format that users can operate intuitively. Users logged into the terminal can easily view email summaries, project progress, and summaries of related documents.

[0129] Authentication information verification method

[0130] The server uses the user's authentication information (e.g., username and password) and sends it to the authentication server. After successful authentication, it obtains a token to access the data.

[0131] Analysis methods using generative AI models

[0132] The server analyzes the collected data using an AI model and generates the necessary prompts. For example, it might use a prompt such as, "Extract information about the achievement status of key milestones and the roles of key members in a particular project."

[0133] Specific example

[0134] When User A is transferring to another department and User B is taking over their duties, the server uses User A's credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and retrieves all relevant documents from the document management system. Then, a BERT model is used to extract key information and generate a summary. Finally, this summarized data is organized by category and provided as a user interface. User B can view the dashboard provided by the server via their terminal, gaining a comprehensive overview of all the necessary information at once.

[0135] In this way, the present invention automates the collection, summarization, and organization of information during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0136] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0137] Program processing flow

[0138] Step 1: User Authentication

[0139] The server first obtains the user's authentication information. It sends the username and password entered by the user on the terminal to the authentication server, and if authentication is successful, it obtains an authentication token.

[0140] Input: Username, Password

[0141] Output: Authentication token

[0142] Specific operation: The server receives the username and password from the terminal and sends them to the authentication server. If the authentication server succeeds in authentication, the server receives an authentication token.

[0143] Step 2: Data Collection

[0144] The server uses authentication tokens to collect data from each data source (e.g., email, project management, document management).

[0145] Input: Authentication token

[0146] Output: Email data, project data, document data

[0147] Specific operation: The server uses the Gmail API to retrieve email data, the Trello and Asana APIs to collect project data, and the Google Drive and Dropbox APIs to retrieve document data.

[0148] Step 3: Data Analysis and Summarization

[0149] The server analyzes the collected data using generative AI models such as the BERT model and extracts important information. Based on the extracted information, it generates a summary.

[0150] Input: Email data, project data, document data

[0151] Output: Summary data

[0152] Specific operation: The server inputs each collected data into an NLP model and generates a summary using a prompt (e.g., "Summarize the key points from emails over the past 6 months"). The generated summary data is then saved.

[0153] Step 4: Data organization

[0154] The server organizes the summarized data by category. Email-related summaries are categorized under "Email," project-related summaries under "Project," and document-related summaries under "Document."

[0155] Input: Summary data

[0156] Output: Summary data organized by category

[0157] Specific operation: The server stores the summary data in JSON format and stores the data in the fields corresponding to each category.

[0158] Step 5: Data Provision

[0159] The terminal receives organized data from the server and presents it as a user interface. When a user logs into the terminal, the data is visualized in a dashboard format, allowing them to see the data at a glance.

[0160] Input: Summary data organized by category

[0161] Output: Dashboard-style user interface

[0162] Specific operation: The terminal retrieves data from the server and displays it as a dashboard. The user uses this to view email summaries, project progress, and summaries of related documents.

[0163] As described above, this system automates the entire process from collecting, analyzing, organizing, and displaying user business data, supporting efficient information management and business continuity.

[0164] (Application Example 1)

[0165] 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."

[0166] In factories and manufacturing sites, managing work data, maintenance data, and document data is complex, making it difficult to efficiently transfer information to new operators and technicians. Furthermore, quickly summarizing and organizing important information from large amounts of data is necessary, but doing so manually is extremely time-consuming and labor-intensive. A system is needed to address these challenges.

[0167] 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.

[0168] In this invention, the server includes means for collecting user data, means for collecting user work data, means for collecting user maintenance data, means for collecting user document data, means for analyzing and summarizing each of the collected data using natural language processing, means for organizing the summarized data by category, and means for displaying the organized data as an intuitively operable user interface. This makes it possible to automatically extract, summarize, and organize important information from a large amount of data and efficiently hand it over to new operators or technicians.

[0169] "Data collection means" refers to methods for automatically collecting various types of user data (work data, maintenance data, document data, etc.).

[0170] "Work data" refers to data related to production activities in factories and manufacturing sites, such as the operating status of production lines, error rates, and production quantities.

[0171] "Maintenance data" refers to data related to maintenance management in factories and manufacturing sites, such as periodic inspection history, failure information, and repair history.

[0172] "Document data" refers to document data such as manuals and operating instructions used in factories and manufacturing sites.

[0173] "Natural language processing" is a technology that uses computers to analyze natural language and perform tasks such as summarizing, classifying, and extracting information.

[0174] A "category-based organization method" is a method of classifying and organizing data summarized using natural language processing technology into categories such as work data, maintenance data, and document data.

[0175] A "user interface" refers to a screen or dashboard that allows a user to intuitively operate and view information provided by a system.

[0176] This invention relates to a system for automatically collecting, summarizing, organizing, and providing information based on user business data (work data, maintenance data, document data, etc.). This system consists of a server, terminals, and user operations.

[0177] Program generation

[0178] The system of this invention consists of the following main components:

[0179] 1. Data collection methods:

[0180] The server collects various user data. Specifically, it uses a REST API to automatically retrieve work data (production line operating status, error rate, number of units manufactured, etc.), maintenance data (periodic inspection history, failure information, repair history, etc.), and document data (manuals, operation instructions, etc.).

[0181] 2. Natural language processing methods:

[0182] The server analyzes the collected data using natural language processing techniques. In this process, advanced generative AI models such as BERT (Bidirectional Encoder Representations from Transformers) are used to extract and summarize important information. For example, major errors and areas for improvement are extracted from work data, and frequently occurring faulty parts and repair history summaries are extracted and summarized from maintenance data.

[0183] 3. Categorization methods:

[0184] The server organizes the summarized data by category. Specifically, it classifies and organizes it into categories such as work information, maintenance information, and equipment usage information.

[0185] 4. Means of providing a user interface:

[0186] The organized data is delivered to the user through terminals, smart glasses, and head-mounted displays. This allows the user to access the necessary information through an intuitive dashboard.

[0187] Hardware and software to use

[0188] Hardware:

[0189] Built-in computer of a factory robot

[0190] Smart Glasses

[0191] Head-mounted display

[0192] software:

[0193] Python

[0194] Transformers Library

[0195] REST API Server

[0196] Explanation of the process and specific examples

[0197] The server collects user business data (work data, maintenance data, document data, etc.) via a REST API and performs natural language processing using a generative AI model (BERT). This model automatically summarizes and organizes important information from large amounts of data. The organized data is categorized and displayed in an intuitive dashboard format on terminals, smart glasses, and head-mounted displays.

[0198] Specific example

[0199] A robot in a factory collects all work and maintenance data, as well as documentation on the equipment used, for the past six months, and new operators can view this visualized information using smart glasses.

[0200] Example of a prompt

[0201] The application installed on the factory robot should automatically collect the following data, summarize each piece, and provide it as handover information to the new operator:

[0202] 1. Work data: Production line operating status, error rate, and production volume for the past 6 months.

[0203] 2. Maintenance data: Periodic inspection history, fault information, repair history

[0204] 3. Documentation for the equipment used: Operation manual, instruction manual

[0205] In this way, the present invention streamlines data management and information provision in factories and manufacturing sites, and enables smooth handover of tasks.

[0206] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0207] Step 1:

[0208] The server verifies the user's authentication information and obtains permission to access the data. The input to this process is the user's authentication information (username, password, etc.), and the output is permission to collect data. Specifically, the server verifies the authentication information through an authentication API.

[0209] Step 2:

[0210] The server uses a predetermined communication protocol (such as a REST API) to collect various user data. The input to this process is authenticated user information, and the output includes work data, maintenance data, and document data. Specifically, the server sends requests to multiple API endpoints to retrieve the necessary data.

[0211] Step 3:

[0212] The server analyzes and summarizes the collected data using natural language processing techniques (generative AI models such as BERT). The input to this process is various data collected by the server, and the output is summarized information. Specifically, the server inputs data into the natural language processing model and obtains the summarization result.

[0213] Step 4:

[0214] The server organizes the summarized data into categories. The input to this process is summarized information, and the output is data organized by category. Specifically, the server classifies the summarized data into categories such as "work information," "maintenance information," and "equipment usage information."

[0215] Step 5:

[0216] The server displays the organized data as a user interface. The input to this process is data organized by category, and the output is a dashboard-style interface. Specifically, the server sends the organized data in an appropriate format to terminals, smart glasses, or head-mounted displays.

[0217] Step 6:

[0218] Users view organized information through the provided user interface. The input to this process is a dashboard-style interface, and the output is the user's understanding and use of the information. Specifically, users use a device or smart glasses to view the provided information and take necessary actions.

[0219] 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.

[0220] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information, in addition to a system that combines this with an emotion engine that recognizes the user's emotions.

[0221] System Configuration

[0222] This system includes the following main components:

[0223] 1. Data acquisition methods

[0224] 2. Data summarization means

[0225] 3. Data organization methods

[0226] 4. Means of providing a user interface

[0227] 5. Emotional Engine

[0228] Specific behavior of each component

[0229] Data acquisition methods

[0230] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[0231] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[0232] Data summarization means

[0233] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[0234] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[0235] Data organization methods

[0236] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0237] Emotional Engine

[0238] The server analyzes the user's emotions using an emotion engine. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the user's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[0239] Data summarization and integration of sentiment data

[0240] The server integrates the summarized data with the recognized sentiment data. This generates handover information that reflects the user's emotional state. For example, if the user's emotions in a particular project are negative, that information is also included in the summarized data.

[0241] User interface provisioning means

[0242] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitively operable dashboard. This allows users to easily check handover information and simultaneously understand fluctuations in their emotions.

[0243] Specific example

[0244] Example 1: Data collection and sentiment recognition

[0245] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and gathers all relevant documents from the document management system. Furthermore, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[0246] Example 2: Data summarization and sentiment reflection

[0247] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information about the achievement status of important milestones in a particular project and the division of roles among key members. Simultaneously, sentiment data is analyzed by an emotion engine, and this information is included alongside the summarized data.

[0248] Example 3: Provision to users

[0249] Finally, this summary data and sentiment data are organized and presented as a user interface. User B can view a dashboard provided by the server through their device, allowing them to grasp at a glance the email summary, project progress, related document overview, and User A's sentiment information.

[0250] As described above, the present invention automates information gathering, summarization, and organization during business handover, and also takes into account the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[0254] Step 2:

[0255] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[0256] Step 3:

[0257] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[0258] Step 4:

[0259] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[0260] Step 5:

[0261] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[0262] Step 6:

[0263] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[0264] Step 7:

[0265] The server uses an emotion engine to analyze the user's emotions. It identifies emotions such as positive, negative, and neutral from the wording and content contained in email data and project management data.

[0266] Step 8:

[0267] The server organizes the summarized data into categories. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document." Furthermore, perceived sentiment information is added to each piece of data.

[0268] Step 9:

[0269] The server formats organized data and sentiment data into datasets for delivery as a user interface. It converts them into a format that is easily accessible to users.

[0270] Step 10:

[0271] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[0272] Step 11:

[0273] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, along with recognized sentiment information, to help ensure the continuity of their work.

[0274] Step 12:

[0275] Users refer to the displayed sentiment data to understand the difficulty level and points to note regarding the handover process. For example, they will pay particular attention to projects where many negative emotions are recognized, and take steps to identify problems and implement countermeasures.

[0276] (Example 2)

[0277] 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".

[0278] Conventional business handover systems struggled to efficiently collect, summarize, and organize user business data. Furthermore, they failed to provide information that took user emotions into consideration, leaving challenges in business continuity and efficiency. This resulted in a lack of accuracy and comprehensiveness in handover information, hindering a smooth transition of operations.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following means.

[0280] In this invention, the server includes means for collecting the user's e-mail data, means for collecting the user's project management data, means for collecting the user's document management data, means for analyzing and summarizing the collected data by natural language processing, means for sorting the summarized data by category, means for analyzing the collected data using an emotion engine for recognizing the user's emotion, means for integrating the summarized data and the recognized emotion data, and means for displaying the sorted data and emotion data as a user interface. Thereby, it is possible to efficiently collect, summarize, and sort the user's business data, and further provide comprehensive handover information with added emotion information.

[0281] The "user" is an individual or organization that uses the system.

[0282] The "e-mail data" is information regarding all e-mails sent and received by the user through their e-mail account.

[0283] The "project management data" is information such as the progress status, tasks, and resources of a project obtained from a system used by the user to manage the project.

[0284] The "document management data" is information regarding documents and files obtained from a system used by the user to manage documents.

[0285] "Natural language processing" is a technology for a computer to understand, analyze, and generate human language.

[0286] The "means for summarizing" is a process for extracting important information from the collected data and compressing it into a concise form for providing. <于 <于

[0287] "Methods of organizing by category" refer to the process of grouping and organizing collected and summarized data according to specific classification criteria.

[0288] An "emotion engine" is an algorithm or system used to analyze and identify emotions from a user's text data.

[0289] "Integration" refers to the process of combining summary data and sentiment data and providing them in a user-friendly format.

[0290] A "user interface" is the visual and operational structure that allows a user to access and operate a system.

[0291] This invention relates to a system that automatically collects, summarizes, and organizes user business data and provides it as handover information. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide more comprehensive handover information.

[0292] Data acquisition methods

[0293] The server first verifies the user's authentication information and obtains permission to access the data. Specifically, the server receives the username and password and compares them with the authentication information stored in the database. If authentication is successful, the server uses APIs (Application Programming Interfaces) to retrieve the user's email data, project management data, and document management data from their respective systems. For example, it might use the Gmail API to collect email data, retrieve project data from project management tools such as Trello and Asana, and retrieve document data from document management systems such as Google Drive and Dropbox.

[0294] Data summarization means

[0295] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it uses advanced NLP models such as the BERT model to extract and summarize important information. For example, if there is a large amount of text data about project progress, it will extract the most important points and progress from the text and summarize them concisely.

[0296] Data organization methods

[0297] The server organizes the summarized data by category. Specifically, it classifies and organizes email-related information into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0298] Emotional Engine

[0299] The server uses an emotion engine to analyze the user's emotions. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. Using an emotion recognition algorithm, it extracts the emotions contained in the user's messages and tasks and classifies them into emotion categories such as positive, negative, and neutral.

[0300] Data summarization and integration of sentiment data

[0301] The server integrates the summarized data with the recognized sentiment data. For example, if a user's sentiment is negative in relation to project progress information, that sentiment information is also added to the summarized data.

[0302] User interface provisioning means

[0303] The device receives organized data and sentiment data provided by the server and displays it as an intuitive dashboard. This allows users to easily check handover information and sentiment data.

[0304] Specific Example

[0305] Example 1: Data Collection and Emotion Recognition

[0306] When user B who takes over the business needs to collect the information because user A is transferred, the server accesses each data source using the authentication information of user A. The server obtains all email data for the past six months from the email server, project data in progress from the project management tool, and all relevant documents from the document management system. Next, the emotion engine is used to recognize the emotions contained in the email messages and project tasks and reflect them in the data.

[0307] Example 2: Data Summarization and Emotion Reflection

[0308] The server analyzes the collected data using natural language processing technology, extracts and summarizes the important points. For example, it analyzes and extracts the achievement status of important milestones and the role sharing of key members in a specific project. At the same time, the emotion data analyzed by the emotion engine is also added to the summary data.

[0309] Example 3: Provision to User

[0310] These summary data and emotion data are integrated on the server and transferred to the terminal. User B can view the dashboard provided by the server through the terminal and grasp at a glance the summary content of the emails, the progress of the project, the overview of the relevant documents, and the emotion information of user A.

[0311] Examples of Prompt Sentences

[0312] Please extract the important points from the following information, summarize them in a list, and also classify and show the emotion data.

[0313] 1. Report on Project Progress

[0314] 2. A Large Amount of Email Data

[0315] 3. Data from the document management system

[0316] As described above, the present invention automates the collection, summarization, and organization of information during business handover, and further incorporates the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[0317] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0318] Step 1:

[0319] The server verifies the user's authentication information.

[0320] Input: Username, Password

[0321] Data processing and calculation: The server authenticates the user by comparing the username and password with the information stored in the database.

[0322] Output: Authentication success or failure status

[0323] Specific operation: When a user enters their username and password on the login screen and clicks the "Login" button, the server starts the authentication process. If authentication is successful, a session is started and the process proceeds to the next data collection process.

[0324] Step 2:

[0325] The server collects email data, project management data, and document management data from each system for users who have successfully authenticated.

[0326] Input: Authentication success status, access information for each data source

[0327] Data processing and calculation: The server retrieves data from each system using APIs. For example, it collects email data using the Gmail API, project data using the Trello API, and document data using the Google Drive API.

[0328] Output: Collected datasets (email data, project management data, document management data)

[0329] Specific operation: The server accesses the endpoint of each API and sends a request to retrieve data. If the data is successfully retrieved, it is temporarily stored in the server's database.

[0330] Step 3:

[0331] The server analyzes and summarizes the collected data using natural language processing (NLP) techniques.

[0332] Input: Collected dataset

[0333] Data processing and computation: We use advanced NLP models such as the BERT model to extract important information and summarize it concisely.

[0334] Output: Summarized dataset (email summaries, project summaries, document summaries)

[0335] Specific operation: The NLP engine on the server analyzes the collected data and performs processes such as "summarizing the content of an email in five lines or less."

[0336] Step 4:

[0337] The server organizes the summarized data by category.

[0338] Input: Summarized dataset

[0339] Data processing and calculation: The server classifies data based on data categories ("email," "project," "document," etc.).

[0340] Output: Datasets organized by category

[0341] Specific operation: The server assigns metadata tags (category information) to the summary data and organizes and stores it in the database for each category.

[0342] Step 5:

[0343] The server uses an emotion engine to analyze the collected data and recognize the user's emotions.

[0344] Input: Collected dataset

[0345] Data Processing and Calculation: Using emotion recognition algorithms, extract and classify positive, negative, and neutral emotions from text data.

[0346] Output: Sentiment dataset (sentiment information for emails, projects, and documents)

[0347] Specific operation: The emotion engine analyzes each piece of text data and adds emotion tags to the data, such as "the content of the email is positive."

[0348] Step 6:

[0349] The server integrates summary data and sentiment data.

[0350] Input: Summarized dataset, sentiment dataset

[0351] Data processing and computation: Integrate sentiment information into summarized data to generate a unified dataset.

[0352] Output: Integrated dataset

[0353] Specific operation: The server matches sentiment data with summary data to generate integrated data, such as "Project progress is on track, but user sentiment is negative."

[0354] Step 7:

[0355] The device receives organized data and sentiment data provided by the server and displays it as a dashboard.

[0356] Input: Integrated dataset

[0357] Data processing / calculations: None (only processing for display purposes)

[0358] Output: Dashboard displayed as the user interface

[0359] Specific operation: The terminal receives data from the server and displays it to the user as a dashboard. This dashboard visually displays email summaries, project progress, summaries of related documents, and user sentiment information.

[0360] (Application Example 2)

[0361] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0362] In factories and workplaces, there are challenges in automatically collecting, organizing, and summarizing data, as well as accurately understanding the emotional state of workers. There is a need for a method to efficiently manage on-site work data and monitor workers' stress levels and motivation in real time.

[0363] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting the user's electronic communication data, means for collecting the user's task management data, means for collecting the user's file management data, means for analyzing and summarizing each of the collected data by natural language processing, means for organizing the summarized data by category, means for recognizing the worker's emotional state based on the organized data, means for integrating and displaying the summarized data and the emotional state, and means for displaying the integrated data as a user interface. This enables efficient management of work data at the site and appropriate understanding of the worker's emotional state.

[0364] "User electronic communication data" refers to information exchanged via email and messaging platforms.

[0365] "User task management data" refers to information collected from project management tools, task lists, and scheduling tools.

[0366] "User file management data" refers to information about electronic files that users save and manage, such as documents, spreadsheets, and presentations.

[0367] "Natural language processing" refers to the technology that enables computers to understand, generate, and analyze human language.

[0368] "Methods of summarization" refer to methods of extracting important information using natural language processing techniques and summarizing it concisely.

[0369] "Organizing by category" refers to a method of classifying collected information into specific groups or categories and managing it efficiently.

[0370] "Means of recognizing emotional states" refers to technologies that analyze and judge an individual's emotions using facial recognition technology or voice analysis.

[0371] "Means of integrated display" refers to methods and technologies for combining summary data and sentiment data and displaying them in a single interface.

[0372] "Means of displaying as a user interface" refers to technologies and methods that visually provide information in a format that is easy for users to operate.

[0373] This invention aims to construct a system for the efficient management of work data in factories and workplaces, and for the appropriate understanding of workers' emotional states. The specific implementation method of the system is described below.

[0374] System Configuration

[0375] This system includes the following main components:

[0376] 1. Data acquisition methods

[0377] 2. Data summarization means

[0378] 3. Data organization methods

[0379] 4. Emotional Engine

[0380] 5. Data Integration Means

[0381] 6. Means of providing a user interface

[0382] Data acquisition methods

[0383] The server first verifies the on-site authentication information and obtains permission to access the data. The data collected includes electronic communication data (e.g., emails and messages), task management data (e.g., information from project management tools), and file management data (e.g., documents and spreadsheets).

[0384] Data summarization means

[0385] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information. For example, key points such as working hours, work speed, and success rate are extracted from work logs and sensor data.

[0386] Data organization methods

[0387] The server also includes a means of organizing summarized data into categories. For example, information related to electronic communication is classified and organized into the "Communication" category, information related to task management into the "Tasks" category, and information related to file management into the "Files" category.

[0388] Emotional Engine

[0389] The server uses an emotion engine to analyze the worker's emotional state. The emotion engine recognizes the worker's emotions based on collected files, tasks, and electronic communication data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the worker's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[0390] Data Integration Means

[0391] The server integrates the summarized data with the recognized sentiment data. This generates work information that reflects the worker's emotional state. For example, if a worker's emotions are negative during a particular task, that information is also included in the summarized data.

[0392] User interface provisioning means

[0393] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitive dashboard. This allows work managers to easily check work information and the emotional state of workers.

[0394] Specific hardware and software to be used

[0395] Hardware: Servers, sensors, cameras

[0396] Software: Python, NLP models (BERT model), sentiment recognition algorithms, project management tools

[0397] Data Processing & Data Calculation: Natural Language Processing, Sentiment Analysis, Data Integration and Display

[0398] Specific example

[0399] To determine whether a worker's emotional state is affecting work efficiency, this system operates as follows:

[0400] As a concrete example, you can enter the following prompt:

[0401] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[0402] This will result in the following output:

[0403] Summary of yesterday's assembly line work:

[0404] Tasks: Packaging, labeling

[0405] Completion Status: Packaging - Completed, Labeling - In Progress

[0406] Total working time: 8 hours

[0407] Employee emotional state:

[0408] Packaging: Positive

[0409] Labeling: Negative

[0410] Thus, the present invention supports the evaluation of overall work efficiency and the work environment, from the collection of work data to the analysis of emotional data and the display of integrated data.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The server verifies the authentication information at the site and obtains permission to access the data. In this step, the user's authentication information is taken as input, and permission to collect data is taken as output. Specifically, it communicates with the authentication server and obtains an authentication token.

[0414] Step 2:

[0415] The server collects electronic communication data, task management data, and file management data from their respective systems. In this step, an authentication token is taken as input, and the collected dataset is taken as output. Specifically, API requests are sent to each data source to retrieve the data.

[0416] Step 3:

[0417] The server analyzes and summarizes each collected data using natural language processing techniques. In this step, the collected data is taken as input, and the summarized data is output. Specifically, the BERT model is used to extract important information and perform text summarization.

[0418] Step 4:

[0419] The server organizes the summarized data into categories. In this step, the summarized data is taken as input and the categorized data is output. Specifically, a semantic classification algorithm is used to separate the data into categories.

[0420] Step 5:

[0421] The server uses an emotion engine to recognize the worker's emotional state. In this step, organized data is taken as input and emotional state data is output. Specifically, facial recognition technology and voice analysis are used to analyze emotions and classify them as positive, negative, neutral, etc.

[0422] Step 6:

[0423] The server integrates the summarized data with the recognized sentiment data. In this step, categorical data and sentiment state data are taken as input, and the integrated data is output. Specifically, the two sets of data are mapped and combined into a single data format.

[0424] Step 7:

[0425] The terminal displays the integrated data retrieved from the server as an intuitive dashboard. In this step, the integrated data is taken as input, and the visual display as a dashboard is taken as output. Specifically, this involves generating a GUI and visualizing the data using a web framework.

[0426] Example prompt statements

[0427] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[0428] The above is a detailed explanation of the processing steps in the example program.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Second Embodiment]

[0433] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information.

[0446] System Configuration

[0447] This system includes the following main components:

[0448] 1. Data acquisition methods

[0449] 2. Data summarization means

[0450] 3. Data organization methods

[0451] 4. Means of providing a user interface

[0452] Specific behavior of each component

[0453] Data acquisition methods

[0454] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[0455] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[0456] Data summarization means

[0457] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[0458] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[0459] Data organization methods

[0460] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0461] User interface provisioning means

[0462] The organized data is provided as a user interface on the device for user access. The device receives the organized data retrieved from the server and displays it as an intuitive dashboard. This allows users to easily check the transfer information.

[0463] Specific example

[0464] Example 1: Data Collection

[0465] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system.

[0466] Example 2: Data summarization and organization

[0467] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information such as the achievement status of important milestones in a particular project and the division of roles among key members.

[0468] Example 3: Provision to users

[0469] Finally, this summarized data is organized and presented as a user interface. User B can access a dashboard provided by the server via their terminal, allowing them to quickly grasp the email summary, project progress, and an overview of related documents.

[0470] As described above, the present invention automates information gathering, summarization, and organization during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[0474] Step 2:

[0475] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[0476] Step 3:

[0477] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[0478] Step 4:

[0479] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[0480] Step 5:

[0481] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[0482] Step 6:

[0483] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[0484] Step 7:

[0485] The server organizes the summarized data by category. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document."

[0486] Step 8:

[0487] The server formats the organized data into datasets for delivery as a user interface. It converts it into a format that users can easily access.

[0488] Step 9:

[0489] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[0490] Step 10:

[0491] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, which helps ensure the continuity of their work.

[0492] (Example 1)

[0493] 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."

[0494] Gathering, summarizing, and organizing information from numerous different data sources during a business handover is extremely time-consuming and laborious. Manually processing a wide range of data, particularly emails, project management, and document management, is cumbersome and prone to errors. Furthermore, there is a need for an effective method to present this information to users efficiently.

[0495] 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.

[0496] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for verifying authentication information and obtaining permission for user data access, means for specifying data categories and displaying the organized data as a user interface, and means for analyzing the collected data using a generation AI model and generating prompt sentences. This makes it possible to automate data collection, analysis, summarization, organization, and display during business handover, significantly reducing time and effort.

[0497] Definitions of important words

[0498] "Means of collecting user email data" refers to a function that retrieves email data from the email server used by the user via an API or similar means.

[0499] "Means for collecting user project management data" refers to a function that retrieves project data from the project management tool used by the user via APIs or other means.

[0500] "Means for collecting user document management data" refers to the function of obtaining document data from the document management system used by the user via APIs or other means.

[0501] "Means for analyzing and summarizing collected data using natural language processing" refers to a function that analyzes acquired data using machine learning and natural language processing techniques, extracts only the important information, and generates a summary.

[0502] "Means for organizing summarized data by category" refers to a function that classifies and organizes the generated summary text into categories such as emails, projects, and documents.

[0503] "Means of verifying authentication information and obtaining permission for user data access" refers to a function that verifies the user's authentication information, authenticates that they are a legitimate user, and then obtains the authority to access the data.

[0504] "A means of specifying data categories and displaying organized data as a user interface" refers to a function that displays organized data by category and provides it in a dashboard-style user interface that allows users to operate it intuitively.

[0505] "A means of analyzing collected data using a generative AI model and generating prompt sentences" refers to a function that analyzes collected data using an advanced AI model and generates the necessary prompt sentences.

[0506] "Means of using predetermined communication protocols" refers to a function that uses standardized protocols when sending and receiving data to ensure secure and efficient communication.

[0507] Modes for carrying out the invention

[0508] This invention is a system that automatically collects various business data from users, summarizes and organizes it using natural language processing (NLP) technology, and provides it to the user. The following describes how this system is specifically implemented.

[0509] System Configuration

[0510] This system includes the following main components.

[0511] 1. Data acquisition methods

[0512] 2. Data summarization means

[0513] 3. Data organization methods

[0514] 4. Means of providing a user interface

[0515] 5. Authentication Information Verification Method

[0516] 6. Analysis methods using generative AI models

[0517] Hardware and software to be used

[0518] The system uses the following hardware and software:

[0519] hardware

[0520] Server: The central hardware responsible for data collection, analysis, and organization.

[0521] Device: A device used by the user to view data (PC, tablet, smartphone, etc.)

[0522] software

[0523] Cloud infrastructure such as GCP (Google Cloud Platform) and AWS (Amazon Web Services)

[0524] Various data source APIs such as Gmail API, Trello API, and Google Drive API

[0525] Natural language processing models such as the BERT model

[0526] HTTP and HTTPS communication protocols

[0527] Data acquisition methods

[0528] The server first verifies the user's authentication information and obtains permission to access the data. Next, it collects data from each data source (e.g., email server, project management tool, document management system). For example, it can retrieve email data via the Gmail API and collect project data using the Trello or Asana APIs. It can also retrieve document data from Google Drive or Dropbox.

[0529] Data summarization means

[0530] The server analyzes the collected data using natural language processing techniques, extracting and summarizing important information. Generative AI models such as the BERT model are used for this process. For example, the following prompt statements are used:

[0531] "Please summarize the key points from your emails over the past six months."

[0532] Data organization methods

[0533] The summarized data is organized by category. For example, email summaries are categorized under "Emails," project progress data under "Projects," and document-related summaries under "Documents."

[0534] User interface provisioning means

[0535] The terminal receives organized data from the server and presents it as a user interface. Specifically, it is displayed in a dashboard format that users can operate intuitively. Users logged into the terminal can easily view email summaries, project progress, and summaries of related documents.

[0536] Authentication information verification method

[0537] The server uses the user's authentication information (e.g., username and password) and sends it to the authentication server. After successful authentication, it obtains a token to access the data.

[0538] Analysis methods using generative AI models

[0539] The server analyzes the collected data using an AI model and generates the necessary prompts. For example, it might use a prompt such as, "Extract information about the achievement status of key milestones and the roles of key members in a particular project."

[0540] Specific example

[0541] When User A is transferring to another department and User B is taking over their duties, the server uses User A's credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and retrieves all relevant documents from the document management system. Then, a BERT model is used to extract key information and generate a summary. Finally, this summarized data is organized by category and provided as a user interface. User B can view the dashboard provided by the server via their terminal, gaining a comprehensive overview of all the necessary information at once.

[0542] In this way, the present invention automates the collection, summarization, and organization of information during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0543] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0544] Program processing flow

[0545] Step 1: User Authentication

[0546] The server first obtains the user's authentication information. It sends the username and password entered by the user on the terminal to the authentication server, and if authentication is successful, it obtains an authentication token.

[0547] Input: Username, Password

[0548] Output: Authentication token

[0549] Specific operation: The server receives the username and password from the terminal and sends them to the authentication server. If the authentication server succeeds in authentication, the server receives an authentication token.

[0550] Step 2: Data Collection

[0551] The server uses authentication tokens to collect data from each data source (e.g., email, project management, document management).

[0552] Input: Authentication token

[0553] Output: Email data, project data, document data

[0554] Specific operation: The server uses the Gmail API to retrieve email data, the Trello and Asana APIs to collect project data, and the Google Drive and Dropbox APIs to retrieve document data.

[0555] Step 3: Data Analysis and Summarization

[0556] The server analyzes the collected data using generative AI models such as the BERT model and extracts important information. Based on the extracted information, it generates a summary.

[0557] Input: Email data, project data, document data

[0558] Output: Summary data

[0559] Specific operation: The server inputs each collected data into an NLP model and generates a summary using a prompt (e.g., "Summarize the key points from emails over the past 6 months"). The generated summary data is then saved.

[0560] Step 4: Data organization

[0561] The server organizes the summarized data by category. Email-related summaries are categorized under "Email," project-related summaries under "Project," and document-related summaries under "Document."

[0562] Input: Summary data

[0563] Output: Summary data organized by category

[0564] Specific operation: The server stores the summary data in JSON format and stores the data in the fields corresponding to each category.

[0565] Step 5: Data Provision

[0566] The terminal receives organized data from the server and presents it as a user interface. When a user logs into the terminal, the data is visualized in a dashboard format, allowing them to see the data at a glance.

[0567] Input: Summary data organized by category

[0568] Output: Dashboard-style user interface

[0569] Specific operation: The terminal retrieves data from the server and displays it as a dashboard. The user uses this to view email summaries, project progress, and summaries of related documents.

[0570] As described above, this system automates the entire process from collecting, analyzing, organizing, and displaying user business data, supporting efficient information management and business continuity.

[0571] (Application Example 1)

[0572] 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."

[0573] In factories and manufacturing sites, managing work data, maintenance data, and document data is complex, making it difficult to efficiently transfer information to new operators and technicians. Furthermore, quickly summarizing and organizing important information from large amounts of data is necessary, but doing so manually is extremely time-consuming and labor-intensive. A system is needed to address these challenges.

[0574] 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.

[0575] In this invention, the server includes means for collecting user data, means for collecting user work data, means for collecting user maintenance data, means for collecting user document data, means for analyzing and summarizing each of the collected data using natural language processing, means for organizing the summarized data by category, and means for displaying the organized data as an intuitively operable user interface. This makes it possible to automatically extract, summarize, and organize important information from a large amount of data and efficiently hand it over to new operators or technicians.

[0576] "Data collection means" refers to methods for automatically collecting various types of user data (work data, maintenance data, document data, etc.).

[0577] "Work data" refers to data related to production activities in factories and manufacturing sites, such as the operating status of production lines, error rates, and production quantities.

[0578] "Maintenance data" refers to data related to maintenance management in factories and manufacturing sites, such as periodic inspection history, failure information, and repair history.

[0579] "Document data" refers to document data such as manuals and operating instructions used in factories and manufacturing sites.

[0580] "Natural language processing" is a technology that uses computers to analyze natural language and perform tasks such as summarizing, classifying, and extracting information.

[0581] A "category-based organization method" is a method of classifying and organizing data summarized using natural language processing technology into categories such as work data, maintenance data, and document data.

[0582] A "user interface" refers to a screen or dashboard that allows a user to intuitively operate and view information provided by a system.

[0583] This invention relates to a system for automatically collecting, summarizing, organizing, and providing information based on user business data (work data, maintenance data, document data, etc.). This system consists of a server, terminals, and user operations.

[0584] Program generation

[0585] The system of this invention consists of the following main components:

[0586] 1. Data collection methods:

[0587] The server collects various user data. Specifically, it uses a REST API to automatically retrieve work data (production line operating status, error rate, number of units manufactured, etc.), maintenance data (periodic inspection history, failure information, repair history, etc.), and document data (manuals, operation instructions, etc.).

[0588] 2. Natural language processing methods:

[0589] The server analyzes the collected data using natural language processing techniques. In this process, advanced generative AI models such as BERT (Bidirectional Encoder Representations from Transformers) are used to extract and summarize important information. For example, major errors and areas for improvement are extracted from work data, and frequently occurring faulty parts and repair history summaries are extracted and summarized from maintenance data.

[0590] 3. Categorization methods:

[0591] The server organizes the summarized data by category. Specifically, it classifies and organizes it into categories such as work information, maintenance information, and equipment usage information.

[0592] 4. Means of providing a user interface:

[0593] The organized data is delivered to the user through terminals, smart glasses, and head-mounted displays. This allows the user to access the necessary information through an intuitive dashboard.

[0594] Hardware and software to use

[0595] Hardware:

[0596] Built-in computer of a factory robot

[0597] Smart Glasses

[0598] Head-mounted display

[0599] software:

[0600] Python

[0601] Transformers Library

[0602] REST API Server

[0603] Explanation of the process and specific examples

[0604] The server collects user business data (work data, maintenance data, document data, etc.) via a REST API and performs natural language processing using a generative AI model (BERT). This model automatically summarizes and organizes important information from large amounts of data. The organized data is categorized and displayed in an intuitive dashboard format on terminals, smart glasses, and head-mounted displays.

[0605] Specific example

[0606] A robot in a factory collects all work and maintenance data, as well as documentation on the equipment used, for the past six months, and new operators can view this visualized information using smart glasses.

[0607] Example of a prompt

[0608] The application installed on the factory robot should automatically collect the following data, summarize each piece, and provide it as handover information to the new operator:

[0609] 1. Work data: Production line operating status, error rate, and production volume for the past 6 months.

[0610] 2. Maintenance data: Periodic inspection history, fault information, repair history

[0611] 3. Documentation for the equipment used: Operation manual, instruction manual

[0612] In this way, the present invention streamlines data management and information provision in factories and manufacturing sites, and enables smooth handover of tasks.

[0613] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0614] Step 1:

[0615] The server verifies the user's authentication information and obtains permission to access the data. The input to this process is the user's authentication information (username, password, etc.), and the output is permission to collect data. Specifically, the server verifies the authentication information through an authentication API.

[0616] Step 2:

[0617] The server uses a predetermined communication protocol (such as a REST API) to collect various user data. The input to this process is authenticated user information, and the output includes work data, maintenance data, and document data. Specifically, the server sends requests to multiple API endpoints to retrieve the necessary data.

[0618] Step 3:

[0619] The server analyzes and summarizes the collected data using natural language processing techniques (generative AI models such as BERT). The input to this process is various data collected by the server, and the output is summarized information. Specifically, the server inputs data into the natural language processing model and obtains the summarization result.

[0620] Step 4:

[0621] The server organizes the summarized data into categories. The input to this process is summarized information, and the output is data organized by category. Specifically, the server classifies the summarized data into categories such as "work information," "maintenance information," and "equipment usage information."

[0622] Step 5:

[0623] The server displays the organized data as a user interface. The input to this process is data organized by category, and the output is a dashboard-style interface. Specifically, the server sends the organized data in an appropriate format to terminals, smart glasses, or head-mounted displays.

[0624] Step 6:

[0625] Users view organized information through the provided user interface. The input to this process is a dashboard-style interface, and the output is the user's understanding and use of the information. Specifically, users use a device or smart glasses to view the provided information and take necessary actions.

[0626] 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.

[0627] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information, in addition to a system that combines this with an emotion engine that recognizes the user's emotions.

[0628] System Configuration

[0629] This system includes the following main components:

[0630] 1. Data acquisition methods

[0631] 2. Data summarization means

[0632] 3. Data organization methods

[0633] 4. Means of providing a user interface

[0634] 5. Emotional Engine

[0635] Specific behavior of each component

[0636] Data acquisition methods

[0637] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[0638] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[0639] Data summarization means

[0640] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[0641] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[0642] Data organization methods

[0643] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0644] Emotional Engine

[0645] The server analyzes the user's emotions using an emotion engine. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the user's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[0646] Data summarization and integration of sentiment data

[0647] The server integrates the summarized data with the recognized sentiment data. This generates handover information that reflects the user's emotional state. For example, if the user's emotions in a particular project are negative, that information is also included in the summarized data.

[0648] User interface provisioning means

[0649] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitively operable dashboard. This allows users to easily check handover information and simultaneously understand fluctuations in their emotions.

[0650] Specific example

[0651] Example 1: Data collection and sentiment recognition

[0652] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and gathers all relevant documents from the document management system. Furthermore, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[0653] Example 2: Data summarization and sentiment reflection

[0654] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information about the achievement status of important milestones in a particular project and the division of roles among key members. Simultaneously, sentiment data is analyzed by an emotion engine, and this information is included alongside the summarized data.

[0655] Example 3: Provision to users

[0656] Finally, this summary data and sentiment data are organized and presented as a user interface. User B can view a dashboard provided by the server through their device, allowing them to grasp at a glance the email summary, project progress, related document overview, and User A's sentiment information.

[0657] As described above, the present invention automates information gathering, summarization, and organization during business handover, and also takes into account the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[0661] Step 2:

[0662] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[0663] Step 3:

[0664] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[0665] Step 4:

[0666] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[0667] Step 5:

[0668] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[0669] Step 6:

[0670] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[0671] Step 7:

[0672] The server uses an emotion engine to analyze the user's emotions. It identifies emotions such as positive, negative, and neutral from the wording and content contained in email data and project management data.

[0673] Step 8:

[0674] The server organizes the summarized data into categories. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document." Furthermore, perceived sentiment information is added to each piece of data.

[0675] Step 9:

[0676] The server formats organized data and sentiment data into datasets for delivery as a user interface. It converts them into a format that is easily accessible to users.

[0677] Step 10:

[0678] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[0679] Step 11:

[0680] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, along with recognized sentiment information, to help ensure the continuity of their work.

[0681] Step 12:

[0682] Users refer to the displayed sentiment data to understand the difficulty level and points to note regarding the handover process. For example, they will pay particular attention to projects where many negative emotions are recognized, and take steps to identify problems and implement countermeasures.

[0683] (Example 2)

[0684] 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".

[0685] Conventional business handover systems struggled to efficiently collect, summarize, and organize user business data. Furthermore, they failed to provide information that took user emotions into consideration, leaving challenges in business continuity and efficiency. This resulted in a lack of accuracy and comprehensiveness in handover information, hindering a smooth transition of operations.

[0686] 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.

[0687] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for analyzing the collected data using an emotion engine that recognizes user emotions, means for integrating the summarized data and recognized emotion data, and means for displaying the organized data and emotion data as a user interface. This makes it possible to efficiently collect, summarize, and organize user business data, and to provide comprehensive handover information with added emotion information.

[0688] A "user" is an individual or organization that uses the system.

[0689] "Email data" refers to all information about emails sent and received by a user through their email account.

[0690] "Project management data" refers to information such as project progress, tasks, and resources, obtained from systems used by users to manage projects.

[0691] "Document management data" refers to information about documents and files obtained from systems used by users to manage their documents.

[0692] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[0693] "Methods of summarization" refer to the process of extracting essential information from collected data and compressing it into a concise format for presentation.

[0694] "Methods of organizing by category" refer to the process of grouping and organizing collected and summarized data according to specific classification criteria.

[0695] An "emotion engine" is an algorithm or system used to analyze and identify emotions from a user's text data.

[0696] "Integration" refers to the process of combining summary data and sentiment data and providing them in a user-friendly format.

[0697] A "user interface" is the visual and operational structure that allows a user to access and operate a system.

[0698] This invention relates to a system that automatically collects, summarizes, and organizes user business data and provides it as handover information. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide more comprehensive handover information.

[0699] Data acquisition methods

[0700] The server first verifies the user's authentication information and obtains permission to access the data. Specifically, the server receives the username and password and compares them with the authentication information stored in the database. If authentication is successful, the server uses APIs (Application Programming Interfaces) to retrieve the user's email data, project management data, and document management data from their respective systems. For example, it might use the Gmail API to collect email data, retrieve project data from project management tools such as Trello and Asana, and retrieve document data from document management systems such as Google Drive and Dropbox.

[0701] Data summarization means

[0702] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it uses advanced NLP models such as the BERT model to extract and summarize important information. For example, if there is a large amount of text data about project progress, it will extract the most important points and progress from the text and summarize them concisely.

[0703] Data organization methods

[0704] The server organizes the summarized data by category. Specifically, it classifies and organizes email-related information into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0705] Emotional Engine

[0706] The server uses an emotion engine to analyze the user's emotions. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. Using an emotion recognition algorithm, it extracts the emotions contained in the user's messages and tasks and classifies them into emotion categories such as positive, negative, and neutral.

[0707] Data summarization and integration of sentiment data

[0708] The server integrates the summarized data with the recognized sentiment data. For example, if a user's sentiment is negative in relation to project progress information, that sentiment information is also added to the summarized data.

[0709] User interface provisioning means

[0710] The device receives organized data and sentiment data provided by the server and displays it as an intuitive dashboard. This allows users to easily check handover information and sentiment data.

[0711] Specific example

[0712] Example 1: Data collection and sentiment recognition

[0713] When User A is transferring to another department and User B is taking over their duties, the server uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system. Next, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[0714] Example 2: Data summarization and sentiment reflection

[0715] The server analyzes the collected data using natural language processing technology, extracting and summarizing key points. For example, it analyzes and extracts the achievement status of important milestones in a specific project and the division of roles among key members. Simultaneously, sentiment data analyzed by the sentiment engine is added to the summarized data.

[0716] Example 3: Provision to users

[0717] These summary and sentiment data are integrated on the server and transferred to the terminal. User B can view the dashboard provided by the server through the terminal, and get an overview of the email summary, project progress, related document summaries, and User A's sentiment information at a glance.

[0718] Example of a prompt

[0719] Extract the key points from the following information and summarize them in a bulleted list. Also, categorize and present the sentiment data.

[0720] 1. Project progress report

[0721] 2. Large amounts of email data

[0722] 3. Data from the document management system

[0723] As described above, the present invention automates the collection, summarization, and organization of information during business handover, and further incorporates the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] The server verifies the user's authentication information.

[0727] Input: Username, Password

[0728] Data processing and calculation: The server authenticates the user by comparing the username and password with the information stored in the database.

[0729] Output: Authentication success or failure status

[0730] Specific operation: When a user enters their username and password on the login screen and clicks the "Login" button, the server starts the authentication process. If authentication is successful, a session is started and the process proceeds to the next data collection process.

[0731] Step 2:

[0732] The server collects email data, project management data, and document management data from each system for users who have successfully authenticated.

[0733] Input: Authentication success status, access information for each data source

[0734] Data processing and calculation: The server retrieves data from each system using APIs. For example, it collects email data using the Gmail API, project data using the Trello API, and document data using the Google Drive API.

[0735] Output: Collected datasets (email data, project management data, document management data)

[0736] Specific operation: The server accesses the endpoint of each API and sends a request to retrieve data. If the data is successfully retrieved, it is temporarily stored in the server's database.

[0737] Step 3:

[0738] The server analyzes and summarizes the collected data using natural language processing (NLP) techniques.

[0739] Input: Collected dataset

[0740] Data processing and computation: We use advanced NLP models such as the BERT model to extract important information and summarize it concisely.

[0741] Output: Summarized dataset (email summaries, project summaries, document summaries)

[0742] Specific operation: The NLP engine on the server analyzes the collected data and performs processes such as "summarizing the content of an email in five lines or less."

[0743] Step 4:

[0744] The server organizes the summarized data by category.

[0745] Input: Summarized dataset

[0746] Data processing and calculation: The server classifies data based on data categories ("email," "project," "document," etc.).

[0747] Output: Datasets organized by category

[0748] Specific operation: The server assigns metadata tags (category information) to the summary data and organizes and stores it in the database for each category.

[0749] Step 5:

[0750] The server uses an emotion engine to analyze the collected data and recognize the user's emotions.

[0751] Input: Collected dataset

[0752] Data Processing and Calculation: Using emotion recognition algorithms, extract and classify positive, negative, and neutral emotions from text data.

[0753] Output: Sentiment dataset (sentiment information for emails, projects, and documents)

[0754] Specific operation: The emotion engine analyzes each piece of text data and adds emotion tags to the data, such as "the content of the email is positive."

[0755] Step 6:

[0756] The server integrates summary data and sentiment data.

[0757] Input: Summarized dataset, sentiment dataset

[0758] Data processing and computation: Integrate sentiment information into summarized data to generate a unified dataset.

[0759] Output: Integrated dataset

[0760] Specific operation: The server matches sentiment data with summary data to generate integrated data, such as "Project progress is on track, but user sentiment is negative."

[0761] Step 7:

[0762] The device receives organized data and sentiment data provided by the server and displays it as a dashboard.

[0763] Input: Integrated dataset

[0764] Data processing / calculations: None (only processing for display purposes)

[0765] Output: Dashboard displayed as the user interface

[0766] Specific operation: The terminal receives data from the server and displays it to the user as a dashboard. This dashboard visually displays email summaries, project progress, summaries of related documents, and user sentiment information.

[0767] (Application Example 2)

[0768] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0769] In factories and workplaces, there are challenges in automatically collecting, organizing, and summarizing data, as well as accurately understanding the emotional state of workers. There is a need for a method to efficiently manage on-site work data and monitor workers' stress levels and motivation in real time.

[0770] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting the user's electronic communication data, means for collecting the user's task management data, means for collecting the user's file management data, means for analyzing and summarizing each of the collected data by natural language processing, means for organizing the summarized data by category, means for recognizing the worker's emotional state based on the organized data, means for integrating and displaying the summarized data and the emotional state, and means for displaying the integrated data as a user interface. This enables efficient management of work data at the site and appropriate understanding of the worker's emotional state.

[0771] "User electronic communication data" refers to information exchanged via email and messaging platforms.

[0772] "User task management data" refers to information collected from project management tools, task lists, and scheduling tools.

[0773] "User file management data" refers to information about electronic files that users save and manage, such as documents, spreadsheets, and presentations.

[0774] "Natural language processing" refers to the technology that enables computers to understand, generate, and analyze human language.

[0775] "Methods of summarization" refer to methods of extracting important information using natural language processing techniques and summarizing it concisely.

[0776] "Organizing by category" refers to a method of classifying collected information into specific groups or categories and managing it efficiently.

[0777] "Means of recognizing emotional states" refers to technologies that analyze and judge an individual's emotions using facial recognition technology or voice analysis.

[0778] "Means of integrated display" refers to methods and technologies for combining summary data and sentiment data and displaying them in a single interface.

[0779] "Means of displaying as a user interface" refers to technologies and methods that visually provide information in a format that is easy for users to operate.

[0780] This invention aims to construct a system for the efficient management of work data in factories and workplaces, and for the appropriate understanding of workers' emotional states. The specific implementation method of the system is described below.

[0781] System Configuration

[0782] This system includes the following main components:

[0783] 1. Data acquisition methods

[0784] 2. Data summarization means

[0785] 3. Data organization methods

[0786] 4. Emotional Engine

[0787] 5. Data Integration Means

[0788] 6. Means of providing a user interface

[0789] Data acquisition methods

[0790] The server first verifies the on-site authentication information and obtains permission to access the data. The data collected includes electronic communication data (e.g., emails and messages), task management data (e.g., information from project management tools), and file management data (e.g., documents and spreadsheets).

[0791] Data summarization means

[0792] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information. For example, key points such as working hours, work speed, and success rate are extracted from work logs and sensor data.

[0793] Data organization methods

[0794] The server also includes a means of organizing summarized data into categories. For example, information related to electronic communication is classified and organized into the "Communication" category, information related to task management into the "Tasks" category, and information related to file management into the "Files" category.

[0795] Emotional Engine

[0796] The server uses an emotion engine to analyze the worker's emotional state. The emotion engine recognizes the worker's emotions based on collected files, tasks, and electronic communication data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the worker's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[0797] Data Integration Means

[0798] The server integrates the summarized data with the recognized sentiment data. This generates work information that reflects the worker's emotional state. For example, if a worker's emotions are negative during a particular task, that information is also included in the summarized data.

[0799] User interface provisioning means

[0800] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitive dashboard. This allows work managers to easily check work information and the emotional state of workers.

[0801] Specific hardware and software to be used

[0802] Hardware: Servers, sensors, cameras

[0803] Software: Python, NLP models (BERT model), sentiment recognition algorithms, project management tools

[0804] Data Processing & Data Calculation: Natural Language Processing, Sentiment Analysis, Data Integration and Display

[0805] Specific example

[0806] To determine whether a worker's emotional state is affecting work efficiency, this system operates as follows:

[0807] As a concrete example, you can enter the following prompt:

[0808] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[0809] This will result in the following output:

[0810] Summary of yesterday's assembly line work:

[0811] Tasks: Packaging, labeling

[0812] Completion Status: Packaging - Completed, Labeling - In Progress

[0813] Total working time: 8 hours

[0814] Employee emotional state:

[0815] Packaging: Positive

[0816] Labeling: Negative

[0817] Thus, the present invention supports the evaluation of overall work efficiency and the work environment, from the collection of work data to the analysis of emotional data and the display of integrated data.

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] The server verifies the authentication information at the site and obtains permission to access the data. In this step, the user's authentication information is taken as input, and permission to collect data is taken as output. Specifically, it communicates with the authentication server and obtains an authentication token.

[0821] Step 2:

[0822] The server collects electronic communication data, task management data, and file management data from their respective systems. In this step, an authentication token is taken as input, and the collected dataset is taken as output. Specifically, API requests are sent to each data source to retrieve the data.

[0823] Step 3:

[0824] The server analyzes and summarizes each collected data using natural language processing techniques. In this step, the collected data is taken as input, and the summarized data is output. Specifically, the BERT model is used to extract important information and perform text summarization.

[0825] Step 4:

[0826] The server organizes the summarized data into categories. In this step, the summarized data is taken as input and the categorized data is output. Specifically, a semantic classification algorithm is used to separate the data into categories.

[0827] Step 5:

[0828] The server uses an emotion engine to recognize the worker's emotional state. In this step, organized data is taken as input and emotional state data is output. Specifically, facial recognition technology and voice analysis are used to analyze emotions and classify them as positive, negative, neutral, etc.

[0829] Step 6:

[0830] The server integrates the summarized data with the recognized sentiment data. In this step, categorical data and sentiment state data are taken as input, and the integrated data is output. Specifically, the two sets of data are mapped and combined into a single data format.

[0831] Step 7:

[0832] The terminal displays the integrated data retrieved from the server as an intuitive dashboard. In this step, the integrated data is taken as input, and the visual display as a dashboard is taken as output. Specifically, this involves generating a GUI and visualizing the data using a web framework.

[0833] Example prompt statements

[0834] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[0835] The above is a detailed explanation of the processing steps in the example program.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] [Third Embodiment]

[0840] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0841] 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.

[0842] 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).

[0843] 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.

[0844] 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.

[0845] 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).

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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".

[0852] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information.

[0853] System Configuration

[0854] This system includes the following main components:

[0855] 1. Data acquisition methods

[0856] 2. Data summarization means

[0857] 3. Data organization methods

[0858] 4. Means of providing a user interface

[0859] Specific behavior of each component

[0860] Data acquisition methods

[0861] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[0862] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[0863] Data summarization means

[0864] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[0865] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[0866] Data organization methods

[0867] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[0868] User interface provisioning means

[0869] The organized data is provided as a user interface on the device for user access. The device receives the organized data retrieved from the server and displays it as an intuitive dashboard. This allows users to easily check the transfer information.

[0870] Specific example

[0871] Example 1: Data Collection

[0872] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system.

[0873] Example 2: Data summarization and organization

[0874] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information such as the achievement status of important milestones in a particular project and the division of roles among key members.

[0875] Example 3: Provision to users

[0876] Finally, this summarized data is organized and presented as a user interface. User B can access a dashboard provided by the server via their terminal, allowing them to quickly grasp the email summary, project progress, and an overview of related documents.

[0877] As described above, the present invention automates information gathering, summarization, and organization during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0878] The following describes the processing flow.

[0879] Step 1:

[0880] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[0881] Step 2:

[0882] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[0883] Step 3:

[0884] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[0885] Step 4:

[0886] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[0887] Step 5:

[0888] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[0889] Step 6:

[0890] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[0891] Step 7:

[0892] The server organizes the summarized data by category. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document."

[0893] Step 8:

[0894] The server formats the organized data into datasets for delivery as a user interface. It converts it into a format that users can easily access.

[0895] Step 9:

[0896] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[0897] Step 10:

[0898] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, which helps ensure the continuity of their work.

[0899] (Example 1)

[0900] 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."

[0901] Gathering, summarizing, and organizing information from numerous different data sources during a business handover is extremely time-consuming and laborious. Manually processing a wide range of data, particularly emails, project management, and document management, is cumbersome and prone to errors. Furthermore, there is a need for an effective method to present this information to users efficiently.

[0902] 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.

[0903] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for verifying authentication information and obtaining permission for user data access, means for specifying data categories and displaying the organized data as a user interface, and means for analyzing the collected data using a generation AI model and generating prompt sentences. This makes it possible to automate data collection, analysis, summarization, organization, and display during business handover, significantly reducing time and effort.

[0904] Definitions of important words

[0905] "Means of collecting user email data" refers to a function that retrieves email data from the email server used by the user via an API or similar means.

[0906] "Means for collecting user project management data" refers to a function that retrieves project data from the project management tool used by the user via APIs or other means.

[0907] "Means for collecting user document management data" refers to the function of obtaining document data from the document management system used by the user via APIs or other means.

[0908] "Means for analyzing and summarizing collected data using natural language processing" refers to a function that analyzes acquired data using machine learning and natural language processing techniques, extracts only the important information, and generates a summary.

[0909] "Means for organizing summarized data by category" refers to a function that classifies and organizes the generated summary text into categories such as emails, projects, and documents.

[0910] "Means of verifying authentication information and obtaining permission for user data access" refers to a function that verifies the user's authentication information, authenticates that they are a legitimate user, and then obtains the authority to access the data.

[0911] "A means of specifying data categories and displaying organized data as a user interface" refers to a function that displays organized data by category and provides it in a dashboard-style user interface that allows users to operate it intuitively.

[0912] "A means of analyzing collected data using a generative AI model and generating prompt sentences" refers to a function that analyzes collected data using an advanced AI model and generates the necessary prompt sentences.

[0913] "Means of using predetermined communication protocols" refers to a function that uses standardized protocols when sending and receiving data to ensure secure and efficient communication.

[0914] Modes for carrying out the invention

[0915] This invention is a system that automatically collects various business data from users, summarizes and organizes it using natural language processing (NLP) technology, and provides it to the user. The following describes how this system is specifically implemented.

[0916] System Configuration

[0917] This system includes the following main components.

[0918] 1. Data acquisition methods

[0919] 2. Data summarization means

[0920] 3. Data organization methods

[0921] 4. Means of providing a user interface

[0922] 5. Authentication Information Verification Method

[0923] 6. Analysis methods using generative AI models

[0924] Hardware and software to be used

[0925] The system uses the following hardware and software:

[0926] hardware

[0927] Server: The central hardware responsible for data collection, analysis, and organization.

[0928] Device: A device used by the user to view data (PC, tablet, smartphone, etc.)

[0929] software

[0930] Cloud infrastructure such as GCP (Google Cloud Platform) and AWS (Amazon Web Services)

[0931] Various data source APIs such as Gmail API, Trello API, and Google Drive API

[0932] Natural language processing models such as the BERT model

[0933] HTTP and HTTPS communication protocols

[0934] Data acquisition methods

[0935] The server first verifies the user's authentication information and obtains permission to access the data. Next, it collects data from each data source (e.g., email server, project management tool, document management system). For example, it can retrieve email data via the Gmail API and collect project data using the Trello or Asana APIs. It can also retrieve document data from Google Drive or Dropbox.

[0936] Data summarization means

[0937] The server analyzes the collected data using natural language processing techniques, extracting and summarizing important information. Generative AI models such as the BERT model are used for this process. For example, the following prompt statements are used:

[0938] "Please summarize the key points from your emails over the past six months."

[0939] Data organization methods

[0940] The summarized data is organized by category. For example, email summaries are categorized under "Emails," project progress data under "Projects," and document-related summaries under "Documents."

[0941] User interface provisioning means

[0942] The terminal receives organized data from the server and presents it as a user interface. Specifically, it is displayed in a dashboard format that users can operate intuitively. Users logged into the terminal can easily view email summaries, project progress, and summaries of related documents.

[0943] Authentication information verification method

[0944] The server uses the user's authentication information (e.g., username and password) and sends it to the authentication server. After successful authentication, it obtains a token to access the data.

[0945] Analysis methods using generative AI models

[0946] The server analyzes the collected data using an AI model and generates the necessary prompts. For example, it might use a prompt such as, "Extract information about the achievement status of key milestones and the roles of key members in a particular project."

[0947] Specific example

[0948] When User A is transferring to another department and User B is taking over their duties, the server uses User A's credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and retrieves all relevant documents from the document management system. Then, a BERT model is used to extract key information and generate a summary. Finally, this summarized data is organized by category and provided as a user interface. User B can view the dashboard provided by the server via their terminal, gaining a comprehensive overview of all the necessary information at once.

[0949] In this way, the present invention automates the collection, summarization, and organization of information during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[0950] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0951] Program processing flow

[0952] Step 1: User Authentication

[0953] The server first obtains the user's authentication information. It sends the username and password entered by the user on the terminal to the authentication server, and if authentication is successful, it obtains an authentication token.

[0954] Input: Username, Password

[0955] Output: Authentication token

[0956] Specific operation: The server receives the username and password from the terminal and sends them to the authentication server. If the authentication server succeeds in authentication, the server receives an authentication token.

[0957] Step 2: Data Collection

[0958] The server uses authentication tokens to collect data from each data source (e.g., email, project management, document management).

[0959] Input: Authentication token

[0960] Output: Email data, project data, document data

[0961] Specific operation: The server uses the Gmail API to retrieve email data, the Trello and Asana APIs to collect project data, and the Google Drive and Dropbox APIs to retrieve document data.

[0962] Step 3: Data Analysis and Summarization

[0963] The server analyzes the collected data using generative AI models such as the BERT model and extracts important information. Based on the extracted information, it generates a summary.

[0964] Input: Email data, project data, document data

[0965] Output: Summary data

[0966] Specific operation: The server inputs each collected data into an NLP model and generates a summary using a prompt (e.g., "Summarize the key points from emails over the past 6 months"). The generated summary data is then saved.

[0967] Step 4: Data organization

[0968] The server organizes the summarized data by category. Email-related summaries are categorized under "Email," project-related summaries under "Project," and document-related summaries under "Document."

[0969] Input: Summary data

[0970] Output: Summary data organized by category

[0971] Specific operation: The server stores the summary data in JSON format and stores the data in the fields corresponding to each category.

[0972] Step 5: Data Provision

[0973] The terminal receives organized data from the server and presents it as a user interface. When a user logs into the terminal, the data is visualized in a dashboard format, allowing them to see the data at a glance.

[0974] Input: Summary data organized by category

[0975] Output: Dashboard-style user interface

[0976] Specific operation: The terminal retrieves data from the server and displays it as a dashboard. The user uses this to view email summaries, project progress, and summaries of related documents.

[0977] As described above, this system automates the entire process from collecting, analyzing, organizing, and displaying user business data, supporting efficient information management and business continuity.

[0978] (Application Example 1)

[0979] 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."

[0980] In factories and manufacturing sites, managing work data, maintenance data, and document data is complex, making it difficult to efficiently transfer information to new operators and technicians. Furthermore, quickly summarizing and organizing important information from large amounts of data is necessary, but doing so manually is extremely time-consuming and labor-intensive. A system is needed to address these challenges.

[0981] 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.

[0982] In this invention, the server includes means for collecting user data, means for collecting user work data, means for collecting user maintenance data, means for collecting user document data, means for analyzing and summarizing each of the collected data using natural language processing, means for organizing the summarized data by category, and means for displaying the organized data as an intuitively operable user interface. This makes it possible to automatically extract, summarize, and organize important information from a large amount of data and efficiently hand it over to new operators or technicians.

[0983] "Data collection means" refers to methods for automatically collecting various types of user data (work data, maintenance data, document data, etc.).

[0984] "Work data" refers to data related to production activities in factories and manufacturing sites, such as the operating status of production lines, error rates, and production quantities.

[0985] "Maintenance data" refers to data related to maintenance management in factories and manufacturing sites, such as periodic inspection history, failure information, and repair history.

[0986] "Document data" refers to document data such as manuals and operating instructions used in factories and manufacturing sites.

[0987] "Natural language processing" is a technology that uses computers to analyze natural language and perform tasks such as summarizing, classifying, and extracting information.

[0988] A "category-based organization method" is a method of classifying and organizing data summarized using natural language processing technology into categories such as work data, maintenance data, and document data.

[0989] A "user interface" refers to a screen or dashboard that allows a user to intuitively operate and view information provided by a system.

[0990] This invention relates to a system for automatically collecting, summarizing, organizing, and providing information based on user business data (work data, maintenance data, document data, etc.). This system consists of a server, terminals, and user operations.

[0991] Program generation

[0992] The system of this invention consists of the following main components:

[0993] 1. Data collection methods:

[0994] The server collects various user data. Specifically, it uses a REST API to automatically retrieve work data (production line operating status, error rate, number of units manufactured, etc.), maintenance data (periodic inspection history, failure information, repair history, etc.), and document data (manuals, operation instructions, etc.).

[0995] 2. Natural language processing methods:

[0996] The server analyzes the collected data using natural language processing techniques. In this process, advanced generative AI models such as BERT (Bidirectional Encoder Representations from Transformers) are used to extract and summarize important information. For example, major errors and areas for improvement are extracted from work data, and frequently occurring faulty parts and repair history summaries are extracted and summarized from maintenance data.

[0997] 3. Categorization methods:

[0998] The server organizes the summarized data by category. Specifically, it classifies and organizes it into categories such as work information, maintenance information, and equipment usage information.

[0999] 4. Means of providing a user interface:

[1000] The organized data is delivered to the user through terminals, smart glasses, and head-mounted displays. This allows the user to access the necessary information through an intuitive dashboard.

[1001] Hardware and software to use

[1002] Hardware:

[1003] Built-in computer of a factory robot

[1004] Smart Glasses

[1005] Head-mounted display

[1006] software:

[1007] Python

[1008] Transformers Library

[1009] REST API Server

[1010] Explanation of the process and specific examples

[1011] The server collects user business data (work data, maintenance data, document data, etc.) via a REST API and performs natural language processing using a generative AI model (BERT). This model automatically summarizes and organizes important information from large amounts of data. The organized data is categorized and displayed in an intuitive dashboard format on terminals, smart glasses, and head-mounted displays.

[1012] Specific example

[1013] A robot in a factory collects all work and maintenance data, as well as documentation on the equipment used, for the past six months, and new operators can view this visualized information using smart glasses.

[1014] Example of a prompt

[1015] The application installed on the factory robot should automatically collect the following data, summarize each piece, and provide it as handover information to the new operator:

[1016] 1. Work data: Production line operating status, error rate, and production volume for the past 6 months.

[1017] 2. Maintenance data: Periodic inspection history, fault information, repair history

[1018] 3. Documentation for the equipment used: Operation manual, instruction manual

[1019] In this way, the present invention streamlines data management and information provision in factories and manufacturing sites, and enables smooth handover of tasks.

[1020] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1021] Step 1:

[1022] The server verifies the user's authentication information and obtains permission to access the data. The input to this process is the user's authentication information (username, password, etc.), and the output is permission to collect data. Specifically, the server verifies the authentication information through an authentication API.

[1023] Step 2:

[1024] The server uses a predetermined communication protocol (such as a REST API) to collect various user data. The input to this process is authenticated user information, and the output includes work data, maintenance data, and document data. Specifically, the server sends requests to multiple API endpoints to retrieve the necessary data.

[1025] Step 3:

[1026] The server analyzes and summarizes the collected data using natural language processing techniques (generative AI models such as BERT). The input to this process is various data collected by the server, and the output is summarized information. Specifically, the server inputs data into the natural language processing model and obtains the summarization result.

[1027] Step 4:

[1028] The server organizes the summarized data into categories. The input to this process is summarized information, and the output is data organized by category. Specifically, the server classifies the summarized data into categories such as "work information," "maintenance information," and "equipment usage information."

[1029] Step 5:

[1030] The server displays the organized data as a user interface. The input to this process is data organized by category, and the output is a dashboard-style interface. Specifically, the server sends the organized data in an appropriate format to terminals, smart glasses, or head-mounted displays.

[1031] Step 6:

[1032] Users view organized information through the provided user interface. The input to this process is a dashboard-style interface, and the output is the user's understanding and use of the information. Specifically, users use a device or smart glasses to view the provided information and take necessary actions.

[1033] 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.

[1034] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information, in addition to a system that combines this with an emotion engine that recognizes the user's emotions.

[1035] System Configuration

[1036] This system includes the following main components:

[1037] 1. Data acquisition methods

[1038] 2. Data summarization means

[1039] 3. Data organization methods

[1040] 4. Means of providing a user interface

[1041] 5. Emotional Engine

[1042] Specific behavior of each component

[1043] Data acquisition methods

[1044] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[1045] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[1046] Data summarization means

[1047] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[1048] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[1049] Data organization methods

[1050] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[1051] Emotional Engine

[1052] The server analyzes the user's emotions using an emotion engine. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the user's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[1053] Data summarization and integration of sentiment data

[1054] The server integrates the summarized data with the recognized sentiment data. This generates handover information that reflects the user's emotional state. For example, if the user's emotions in a particular project are negative, that information is also included in the summarized data.

[1055] User interface provisioning means

[1056] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitively operable dashboard. This allows users to easily check handover information and simultaneously understand fluctuations in their emotions.

[1057] Specific example

[1058] Example 1: Data collection and sentiment recognition

[1059] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and gathers all relevant documents from the document management system. Furthermore, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[1060] Example 2: Data summarization and sentiment reflection

[1061] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information about the achievement status of important milestones in a particular project and the division of roles among key members. Simultaneously, sentiment data is analyzed by an emotion engine, and this information is included alongside the summarized data.

[1062] Example 3: Provision to users

[1063] Finally, this summary data and sentiment data are organized and presented as a user interface. User B can view a dashboard provided by the server through their device, allowing them to grasp at a glance the email summary, project progress, related document overview, and User A's sentiment information.

[1064] As described above, the present invention automates information gathering, summarization, and organization during business handover, and also takes into account the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[1065] The following describes the processing flow.

[1066] Step 1:

[1067] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[1068] Step 2:

[1069] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[1070] Step 3:

[1071] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[1072] Step 4:

[1073] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[1074] Step 5:

[1075] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[1076] Step 6:

[1077] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[1078] Step 7:

[1079] The server uses an emotion engine to analyze the user's emotions. It identifies emotions such as positive, negative, and neutral from the wording and content contained in email data and project management data.

[1080] Step 8:

[1081] The server organizes the summarized data into categories. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document." Furthermore, perceived sentiment information is added to each piece of data.

[1082] Step 9:

[1083] The server formats organized data and sentiment data into datasets for delivery as a user interface. It converts them into a format that is easily accessible to users.

[1084] Step 10:

[1085] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[1086] Step 11:

[1087] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, along with recognized sentiment information, to help ensure the continuity of their work.

[1088] Step 12:

[1089] Users refer to the displayed sentiment data to understand the difficulty level and points to note regarding the handover process. For example, they will pay particular attention to projects where many negative emotions are recognized, and take steps to identify problems and implement countermeasures.

[1090] (Example 2)

[1091] 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."

[1092] Conventional business handover systems struggled to efficiently collect, summarize, and organize user business data. Furthermore, they failed to provide information that took user emotions into consideration, leaving challenges in business continuity and efficiency. This resulted in a lack of accuracy and comprehensiveness in handover information, hindering a smooth transition of operations.

[1093] 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.

[1094] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for analyzing the collected data using an emotion engine that recognizes user emotions, means for integrating the summarized data and recognized emotion data, and means for displaying the organized data and emotion data as a user interface. This makes it possible to efficiently collect, summarize, and organize user business data, and to provide comprehensive handover information with added emotion information.

[1095] A "user" is an individual or organization that uses the system.

[1096] "Email data" refers to all information about emails sent and received by a user through their email account.

[1097] "Project management data" refers to information such as project progress, tasks, and resources, obtained from systems used by users to manage projects.

[1098] "Document management data" refers to information about documents and files obtained from systems used by users to manage their documents.

[1099] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[1100] "Methods of summarization" refer to the process of extracting essential information from collected data and compressing it into a concise format for presentation.

[1101] "Methods of organizing by category" refer to the process of grouping and organizing collected and summarized data according to specific classification criteria.

[1102] An "emotion engine" is an algorithm or system used to analyze and identify emotions from a user's text data.

[1103] "Integration" refers to the process of combining summary data and sentiment data and providing them in a user-friendly format.

[1104] A "user interface" is the visual and operational structure that allows a user to access and operate a system.

[1105] This invention relates to a system that automatically collects, summarizes, and organizes user business data and provides it as handover information. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide more comprehensive handover information.

[1106] Data acquisition methods

[1107] The server first verifies the user's authentication information and obtains permission to access the data. Specifically, the server receives the username and password and compares them with the authentication information stored in the database. If authentication is successful, the server uses APIs (Application Programming Interfaces) to retrieve the user's email data, project management data, and document management data from their respective systems. For example, it might use the Gmail API to collect email data, retrieve project data from project management tools such as Trello and Asana, and retrieve document data from document management systems such as Google Drive and Dropbox.

[1108] Data summarization means

[1109] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it uses advanced NLP models such as the BERT model to extract and summarize important information. For example, if there is a large amount of text data about project progress, it will extract the most important points and progress from the text and summarize them concisely.

[1110] Data organization methods

[1111] The server organizes the summarized data by category. Specifically, it classifies and organizes email-related information into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[1112] Emotional Engine

[1113] The server uses an emotion engine to analyze the user's emotions. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. Using an emotion recognition algorithm, it extracts the emotions contained in the user's messages and tasks and classifies them into emotion categories such as positive, negative, and neutral.

[1114] Data summarization and integration of sentiment data

[1115] The server integrates the summarized data with the recognized sentiment data. For example, if a user's sentiment is negative in relation to project progress information, that sentiment information is also added to the summarized data.

[1116] User interface provisioning means

[1117] The device receives organized data and sentiment data provided by the server and displays it as an intuitive dashboard. This allows users to easily check handover information and sentiment data.

[1118] Specific example

[1119] Example 1: Data collection and sentiment recognition

[1120] When User A is transferring to another department and User B is taking over their duties, the server uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system. Next, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[1121] Example 2: Data summarization and sentiment reflection

[1122] The server analyzes the collected data using natural language processing technology, extracting and summarizing key points. For example, it analyzes and extracts the achievement status of important milestones in a specific project and the division of roles among key members. Simultaneously, sentiment data analyzed by the sentiment engine is added to the summarized data.

[1123] Example 3: Provision to users

[1124] These summary and sentiment data are integrated on the server and transferred to the terminal. User B can view the dashboard provided by the server through the terminal, and get an overview of the email summary, project progress, related document summaries, and User A's sentiment information at a glance.

[1125] Example of a prompt

[1126] Extract the key points from the following information and summarize them in a bulleted list. Also, categorize and present the sentiment data.

[1127] 1. Project progress report

[1128] 2. Large amounts of email data

[1129] 3. Data from the document management system

[1130] As described above, the present invention automates the collection, summarization, and organization of information during business handover, and further incorporates the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[1131] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1132] Step 1:

[1133] The server verifies the user's authentication information.

[1134] Input: Username, Password

[1135] Data processing and calculation: The server authenticates the user by comparing the username and password with the information stored in the database.

[1136] Output: Authentication success or failure status

[1137] Specific operation: When a user enters their username and password on the login screen and clicks the "Login" button, the server starts the authentication process. If authentication is successful, a session is started and the process proceeds to the next data collection process.

[1138] Step 2:

[1139] The server collects email data, project management data, and document management data from each system for users who have successfully authenticated.

[1140] Input: Authentication success status, access information for each data source

[1141] Data processing and calculation: The server retrieves data from each system using APIs. For example, it collects email data using the Gmail API, project data using the Trello API, and document data using the Google Drive API.

[1142] Output: Collected datasets (email data, project management data, document management data)

[1143] Specific operation: The server accesses the endpoint of each API and sends a request to retrieve data. If the data is successfully retrieved, it is temporarily stored in the server's database.

[1144] Step 3:

[1145] The server analyzes and summarizes the collected data using natural language processing (NLP) techniques.

[1146] Input: Collected dataset

[1147] Data processing and computation: We use advanced NLP models such as the BERT model to extract important information and summarize it concisely.

[1148] Output: Summarized dataset (email summaries, project summaries, document summaries)

[1149] Specific operation: The NLP engine on the server analyzes the collected data and performs processes such as "summarizing the content of an email in five lines or less."

[1150] Step 4:

[1151] The server organizes the summarized data by category.

[1152] Input: Summarized dataset

[1153] Data processing and calculation: The server classifies data based on data categories ("email," "project," "document," etc.).

[1154] Output: Datasets organized by category

[1155] Specific operation: The server assigns metadata tags (category information) to the summary data and organizes and stores it in the database for each category.

[1156] Step 5:

[1157] The server uses an emotion engine to analyze the collected data and recognize the user's emotions.

[1158] Input: Collected dataset

[1159] Data Processing and Calculation: Using emotion recognition algorithms, extract and classify positive, negative, and neutral emotions from text data.

[1160] Output: Sentiment dataset (sentiment information for emails, projects, and documents)

[1161] Specific operation: The emotion engine analyzes each piece of text data and adds emotion tags to the data, such as "the content of the email is positive."

[1162] Step 6:

[1163] The server integrates summary data and sentiment data.

[1164] Input: Summarized dataset, sentiment dataset

[1165] Data processing and computation: Integrate sentiment information into summarized data to generate a unified dataset.

[1166] Output: Integrated dataset

[1167] Specific operation: The server matches sentiment data with summary data to generate integrated data, such as "Project progress is on track, but user sentiment is negative."

[1168] Step 7:

[1169] The device receives organized data and sentiment data provided by the server and displays it as a dashboard.

[1170] Input: Integrated dataset

[1171] Data processing / calculations: None (only processing for display purposes)

[1172] Output: Dashboard displayed as the user interface

[1173] Specific operation: The terminal receives data from the server and displays it to the user as a dashboard. This dashboard visually displays email summaries, project progress, summaries of related documents, and user sentiment information.

[1174] (Application Example 2)

[1175] 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."

[1176] In factories and workplaces, there are challenges in automatically collecting, organizing, and summarizing data, as well as accurately understanding the emotional state of workers. There is a need for a method to efficiently manage on-site work data and monitor workers' stress levels and motivation in real time.

[1177] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting the user's electronic communication data, means for collecting the user's task management data, means for collecting the user's file management data, means for analyzing and summarizing each of the collected data by natural language processing, means for organizing the summarized data by category, means for recognizing the worker's emotional state based on the organized data, means for integrating and displaying the summarized data and the emotional state, and means for displaying the integrated data as a user interface. This enables efficient management of work data at the site and appropriate understanding of the worker's emotional state.

[1178] "User electronic communication data" refers to information exchanged via email and messaging platforms.

[1179] "User task management data" refers to information collected from project management tools, task lists, and scheduling tools.

[1180] "User file management data" refers to information about electronic files that users save and manage, such as documents, spreadsheets, and presentations.

[1181] "Natural language processing" refers to the technology that enables computers to understand, generate, and analyze human language.

[1182] "Methods of summarization" refer to methods of extracting important information using natural language processing techniques and summarizing it concisely.

[1183] "Organizing by category" refers to a method of classifying collected information into specific groups or categories and managing it efficiently.

[1184] "Means of recognizing emotional states" refers to technologies that analyze and judge an individual's emotions using facial recognition technology or voice analysis.

[1185] "Means of integrated display" refers to methods and technologies for combining summary data and sentiment data and displaying them in a single interface.

[1186] "Means of displaying as a user interface" refers to technologies and methods that visually provide information in a format that is easy for users to operate.

[1187] This invention aims to construct a system for the efficient management of work data in factories and workplaces, and for the appropriate understanding of workers' emotional states. The specific implementation method of the system is described below.

[1188] System Configuration

[1189] This system includes the following main components:

[1190] 1. Data acquisition methods

[1191] 2. Data summarization means

[1192] 3. Data organization methods

[1193] 4. Emotional Engine

[1194] 5. Data Integration Means

[1195] 6. Means of providing a user interface

[1196] Data acquisition methods

[1197] The server first verifies the on-site authentication information and obtains permission to access the data. The data collected includes electronic communication data (e.g., emails and messages), task management data (e.g., information from project management tools), and file management data (e.g., documents and spreadsheets).

[1198] Data summarization means

[1199] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information. For example, key points such as working hours, work speed, and success rate are extracted from work logs and sensor data.

[1200] Data organization methods

[1201] The server also includes a means of organizing summarized data into categories. For example, information related to electronic communication is classified and organized into the "Communication" category, information related to task management into the "Tasks" category, and information related to file management into the "Files" category.

[1202] Emotional Engine

[1203] The server uses an emotion engine to analyze the worker's emotional state. The emotion engine recognizes the worker's emotions based on collected files, tasks, and electronic communication data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the worker's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[1204] Data Integration Means

[1205] The server integrates the summarized data with the recognized sentiment data. This generates work information that reflects the worker's emotional state. For example, if a worker's emotions are negative during a particular task, that information is also included in the summarized data.

[1206] User interface provisioning means

[1207] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitive dashboard. This allows work managers to easily check work information and the emotional state of workers.

[1208] Specific hardware and software to be used

[1209] Hardware: Servers, sensors, cameras

[1210] Software: Python, NLP models (BERT model), sentiment recognition algorithms, project management tools

[1211] Data Processing & Data Calculation: Natural Language Processing, Sentiment Analysis, Data Integration and Display

[1212] Specific example

[1213] To determine whether a worker's emotional state is affecting work efficiency, this system operates as follows:

[1214] As a concrete example, you can enter the following prompt:

[1215] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[1216] This will result in the following output:

[1217] Summary of yesterday's assembly line work:

[1218] Tasks: Packaging, labeling

[1219] Completion Status: Packaging - Completed, Labeling - In Progress

[1220] Total working time: 8 hours

[1221] Employee emotional state:

[1222] Packaging: Positive

[1223] Labeling: Negative

[1224] Thus, the present invention supports the evaluation of overall work efficiency and the work environment, from the collection of work data to the analysis of emotional data and the display of integrated data.

[1225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1226] Step 1:

[1227] The server verifies the authentication information at the site and obtains permission to access the data. In this step, the user's authentication information is taken as input, and permission to collect data is taken as output. Specifically, it communicates with the authentication server and obtains an authentication token.

[1228] Step 2:

[1229] The server collects electronic communication data, task management data, and file management data from their respective systems. In this step, an authentication token is taken as input, and the collected dataset is taken as output. Specifically, API requests are sent to each data source to retrieve the data.

[1230] Step 3:

[1231] The server analyzes and summarizes each collected data using natural language processing techniques. In this step, the collected data is taken as input, and the summarized data is output. Specifically, the BERT model is used to extract important information and perform text summarization.

[1232] Step 4:

[1233] The server organizes the summarized data into categories. In this step, the summarized data is taken as input and the categorized data is output. Specifically, a semantic classification algorithm is used to separate the data into categories.

[1234] Step 5:

[1235] The server uses an emotion engine to recognize the worker's emotional state. In this step, organized data is taken as input and emotional state data is output. Specifically, facial recognition technology and voice analysis are used to analyze emotions and classify them as positive, negative, neutral, etc.

[1236] Step 6:

[1237] The server integrates the summarized data with the recognized sentiment data. In this step, categorical data and sentiment state data are taken as input, and the integrated data is output. Specifically, the two sets of data are mapped and combined into a single data format.

[1238] Step 7:

[1239] The terminal displays the integrated data retrieved from the server as an intuitive dashboard. In this step, the integrated data is taken as input, and the visual display as a dashboard is taken as output. Specifically, this involves generating a GUI and visualizing the data using a web framework.

[1240] Example prompt statements

[1241] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[1242] The above is a detailed explanation of the processing steps in the example program.

[1243] 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.

[1244] 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.

[1245] 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.

[1246] [Fourth Embodiment]

[1247] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1248] 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.

[1249] 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).

[1250] 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.

[1251] 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.

[1252] 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).

[1253] 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.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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".

[1260] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information.

[1261] System Configuration

[1262] This system includes the following main components:

[1263] 1. Data acquisition methods

[1264] 2. Data summarization means

[1265] 3. Data organization methods

[1266] 4. Means of providing a user interface

[1267] Specific behavior of each component

[1268] Data acquisition methods

[1269] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[1270] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[1271] Data summarization means

[1272] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[1273] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[1274] Data organization methods

[1275] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[1276] User interface provisioning means

[1277] The organized data is provided as a user interface on the device for user access. The device receives the organized data retrieved from the server and displays it as an intuitive dashboard. This allows users to easily check the transfer information.

[1278] Specific example

[1279] Example 1: Data Collection

[1280] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system.

[1281] Example 2: Data summarization and organization

[1282] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information such as the achievement status of important milestones in a particular project and the division of roles among key members.

[1283] Example 3: Provision to users

[1284] Finally, this summarized data is organized and presented as a user interface. User B can access a dashboard provided by the server via their terminal, allowing them to quickly grasp the email summary, project progress, and an overview of related documents.

[1285] As described above, the present invention automates information gathering, summarization, and organization during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[1286] The following describes the processing flow.

[1287] Step 1:

[1288] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[1289] Step 2:

[1290] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[1291] Step 3:

[1292] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[1293] Step 4:

[1294] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[1295] Step 5:

[1296] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[1297] Step 6:

[1298] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[1299] Step 7:

[1300] The server organizes the summarized data by category. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document."

[1301] Step 8:

[1302] The server formats the organized data into datasets for delivery as a user interface. It converts it into a format that users can easily access.

[1303] Step 9:

[1304] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[1305] Step 10:

[1306] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, which helps ensure the continuity of their work.

[1307] (Example 1)

[1308] 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".

[1309] Gathering, summarizing, and organizing information from numerous different data sources during a business handover is extremely time-consuming and laborious. Manually processing a wide range of data, particularly emails, project management, and document management, is cumbersome and prone to errors. Furthermore, there is a need for an effective method to present this information to users efficiently.

[1310] 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.

[1311] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for verifying authentication information and obtaining permission for user data access, means for specifying data categories and displaying the organized data as a user interface, and means for analyzing the collected data using a generation AI model and generating prompt sentences. This makes it possible to automate data collection, analysis, summarization, organization, and display during business handover, significantly reducing time and effort.

[1312] Definitions of important words

[1313] "Means of collecting user email data" refers to a function that retrieves email data from the email server used by the user via an API or similar means.

[1314] "Means for collecting user project management data" refers to a function that retrieves project data from the project management tool used by the user via APIs or other means.

[1315] "Means for collecting user document management data" refers to the function of obtaining document data from the document management system used by the user via APIs or other means.

[1316] "Means for analyzing and summarizing collected data using natural language processing" refers to a function that analyzes acquired data using machine learning and natural language processing techniques, extracts only the important information, and generates a summary.

[1317] "Means for organizing summarized data by category" refers to a function that classifies and organizes the generated summary text into categories such as emails, projects, and documents.

[1318] "Means of verifying authentication information and obtaining permission for user data access" refers to a function that verifies the user's authentication information, authenticates that they are a legitimate user, and then obtains the authority to access the data.

[1319] "A means of specifying data categories and displaying organized data as a user interface" refers to a function that displays organized data by category and provides it in a dashboard-style user interface that allows users to operate it intuitively.

[1320] "A means of analyzing collected data using a generative AI model and generating prompt sentences" refers to a function that analyzes collected data using an advanced AI model and generates the necessary prompt sentences.

[1321] "Means of using predetermined communication protocols" refers to a function that uses standardized protocols when sending and receiving data to ensure secure and efficient communication.

[1322] Modes for carrying out the invention

[1323] This invention is a system that automatically collects various business data from users, summarizes and organizes it using natural language processing (NLP) technology, and provides it to the user. The following describes how this system is specifically implemented.

[1324] System Configuration

[1325] This system includes the following main components.

[1326] 1. Data acquisition methods

[1327] 2. Data summarization means

[1328] 3. Data organization methods

[1329] 4. Means of providing a user interface

[1330] 5. Authentication Information Verification Method

[1331] 6. Analysis methods using generative AI models

[1332] Hardware and software to be used

[1333] The system uses the following hardware and software:

[1334] hardware

[1335] Server: The central hardware responsible for data collection, analysis, and organization.

[1336] Device: A device used by the user to view data (PC, tablet, smartphone, etc.)

[1337] software

[1338] Cloud infrastructure such as GCP (Google Cloud Platform) and AWS (Amazon Web Services)

[1339] Various data source APIs such as Gmail API, Trello API, and Google Drive API

[1340] Natural language processing models such as the BERT model

[1341] HTTP and HTTPS communication protocols

[1342] Data acquisition methods

[1343] The server first verifies the user's authentication information and obtains permission to access the data. Next, it collects data from each data source (e.g., email server, project management tool, document management system). For example, it can retrieve email data via the Gmail API and collect project data using the Trello or Asana APIs. It can also retrieve document data from Google Drive or Dropbox.

[1344] Data summarization means

[1345] The server analyzes the collected data using natural language processing techniques, extracting and summarizing important information. Generative AI models such as the BERT model are used for this process. For example, the following prompt statements are used:

[1346] "Please summarize the key points from your emails over the past six months."

[1347] Data organization methods

[1348] The summarized data is organized by category. For example, email summaries are categorized under "Emails," project progress data under "Projects," and document-related summaries under "Documents."

[1349] User interface provisioning means

[1350] The terminal receives organized data from the server and presents it as a user interface. Specifically, it is displayed in a dashboard format that users can operate intuitively. Users logged into the terminal can easily view email summaries, project progress, and summaries of related documents.

[1351] Authentication information verification method

[1352] The server uses the user's authentication information (e.g., username and password) and sends it to the authentication server. After successful authentication, it obtains a token to access the data.

[1353] Analysis methods using generative AI models

[1354] The server analyzes the collected data using an AI model and generates the necessary prompts. For example, it might use a prompt such as, "Extract information about the achievement status of key milestones and the roles of key members in a particular project."

[1355] Specific example

[1356] When User A is transferring to another department and User B is taking over their duties, the server uses User A's credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and retrieves all relevant documents from the document management system. Then, a BERT model is used to extract key information and generate a summary. Finally, this summarized data is organized by category and provided as a user interface. User B can view the dashboard provided by the server via their terminal, gaining a comprehensive overview of all the necessary information at once.

[1357] In this way, the present invention automates the collection, summarization, and organization of information during business handover, thereby reducing the burden on users and supporting efficient business continuity.

[1358] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1359] Program processing flow

[1360] Step 1: User Authentication

[1361] The server first obtains the user's authentication information. It sends the username and password entered by the user on the terminal to the authentication server, and if authentication is successful, it obtains an authentication token.

[1362] Input: Username, Password

[1363] Output: Authentication token

[1364] Specific operation: The server receives the username and password from the terminal and sends them to the authentication server. If the authentication server succeeds in authentication, the server receives an authentication token.

[1365] Step 2: Data Collection

[1366] The server uses authentication tokens to collect data from each data source (e.g., email, project management, document management).

[1367] Input: Authentication token

[1368] Output: Email data, project data, document data

[1369] Specific operation: The server uses the Gmail API to retrieve email data, the Trello and Asana APIs to collect project data, and the Google Drive and Dropbox APIs to retrieve document data.

[1370] Step 3: Data Analysis and Summarization

[1371] The server analyzes the collected data using generative AI models such as the BERT model and extracts important information. Based on the extracted information, it generates a summary.

[1372] Input: Email data, project data, document data

[1373] Output: Summary data

[1374] Specific operation: The server inputs each collected data into an NLP model and generates a summary using a prompt (e.g., "Summarize the key points from emails over the past 6 months"). The generated summary data is then saved.

[1375] Step 4: Data organization

[1376] The server organizes the summarized data by category. Email-related summaries are categorized under "Email," project-related summaries under "Project," and document-related summaries under "Document."

[1377] Input: Summary data

[1378] Output: Summary data organized by category

[1379] Specific operation: The server stores the summary data in JSON format and stores the data in the fields corresponding to each category.

[1380] Step 5: Data Provision

[1381] The terminal receives organized data from the server and presents it as a user interface. When a user logs into the terminal, the data is visualized in a dashboard format, allowing them to see the data at a glance.

[1382] Input: Summary data organized by category

[1383] Output: Dashboard-style user interface

[1384] Specific operation: The terminal retrieves data from the server and displays it as a dashboard. The user uses this to view email summaries, project progress, and summaries of related documents.

[1385] As described above, this system automates the entire process from collecting, analyzing, organizing, and displaying user business data, supporting efficient information management and business continuity.

[1386] (Application Example 1)

[1387] 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".

[1388] In factories and manufacturing sites, managing work data, maintenance data, and document data is complex, making it difficult to efficiently transfer information to new operators and technicians. Furthermore, quickly summarizing and organizing important information from large amounts of data is necessary, but doing so manually is extremely time-consuming and labor-intensive. A system is needed to address these challenges.

[1389] 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.

[1390] In this invention, the server includes means for collecting user data, means for collecting user work data, means for collecting user maintenance data, means for collecting user document data, means for analyzing and summarizing each of the collected data using natural language processing, means for organizing the summarized data by category, and means for displaying the organized data as an intuitively operable user interface. This makes it possible to automatically extract, summarize, and organize important information from a large amount of data and efficiently hand it over to new operators or technicians.

[1391] "Data collection means" refers to methods for automatically collecting various types of user data (work data, maintenance data, document data, etc.).

[1392] "Work data" refers to data related to production activities in factories and manufacturing sites, such as the operating status of production lines, error rates, and production quantities.

[1393] "Maintenance data" refers to data related to maintenance management in factories and manufacturing sites, such as periodic inspection history, failure information, and repair history.

[1394] "Document data" refers to document data such as manuals and operating instructions used in factories and manufacturing sites.

[1395] "Natural language processing" is a technology that uses computers to analyze natural language and perform tasks such as summarizing, classifying, and extracting information.

[1396] A "category-based organization method" is a method of classifying and organizing data summarized using natural language processing technology into categories such as work data, maintenance data, and document data.

[1397] A "user interface" refers to a screen or dashboard that allows a user to intuitively operate and view information provided by a system.

[1398] This invention relates to a system for automatically collecting, summarizing, organizing, and providing information based on user business data (work data, maintenance data, document data, etc.). This system consists of a server, terminals, and user operations.

[1399] Program generation

[1400] The system of this invention consists of the following main components:

[1401] 1. Data collection methods:

[1402] The server collects various user data. Specifically, it uses a REST API to automatically retrieve work data (production line operating status, error rate, number of units manufactured, etc.), maintenance data (periodic inspection history, failure information, repair history, etc.), and document data (manuals, operation instructions, etc.).

[1403] 2. Natural language processing methods:

[1404] The server analyzes the collected data using natural language processing techniques. In this process, advanced generative AI models such as BERT (Bidirectional Encoder Representations from Transformers) are used to extract and summarize important information. For example, major errors and areas for improvement are extracted from work data, and frequently occurring faulty parts and repair history summaries are extracted and summarized from maintenance data.

[1405] 3. Categorization methods:

[1406] The server organizes the summarized data by category. Specifically, it classifies and organizes it into categories such as work information, maintenance information, and equipment usage information.

[1407] 4. Means of providing a user interface:

[1408] The organized data is delivered to the user through terminals, smart glasses, and head-mounted displays. This allows the user to access the necessary information through an intuitive dashboard.

[1409] Hardware and software to use

[1410] Hardware:

[1411] Built-in computer of a factory robot

[1412] Smart Glasses

[1413] Head-mounted display

[1414] software:

[1415] Python

[1416] Transformers Library

[1417] REST API Server

[1418] Explanation of the process and specific examples

[1419] The server collects user business data (work data, maintenance data, document data, etc.) via a REST API and performs natural language processing using a generative AI model (BERT). This model automatically summarizes and organizes important information from large amounts of data. The organized data is categorized and displayed in an intuitive dashboard format on terminals, smart glasses, and head-mounted displays.

[1420] Specific example

[1421] A robot in a factory collects all work and maintenance data, as well as documentation on the equipment used, for the past six months, and new operators can view this visualized information using smart glasses.

[1422] Example of a prompt

[1423] The application installed on the factory robot should automatically collect the following data, summarize each piece, and provide it as handover information to the new operator:

[1424] 1. Work data: Production line operating status, error rate, and production volume for the past 6 months.

[1425] 2. Maintenance data: Periodic inspection history, fault information, repair history

[1426] 3. Documentation for the equipment used: Operation manual, instruction manual

[1427] In this way, the present invention streamlines data management and information provision in factories and manufacturing sites, and enables smooth handover of tasks.

[1428] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1429] Step 1:

[1430] The server verifies the user's authentication information and obtains permission to access the data. The input to this process is the user's authentication information (username, password, etc.), and the output is permission to collect data. Specifically, the server verifies the authentication information through an authentication API.

[1431] Step 2:

[1432] The server uses a predetermined communication protocol (such as a REST API) to collect various user data. The input to this process is authenticated user information, and the output includes work data, maintenance data, and document data. Specifically, the server sends requests to multiple API endpoints to retrieve the necessary data.

[1433] Step 3:

[1434] The server analyzes and summarizes the collected data using natural language processing techniques (generative AI models such as BERT). The input to this process is various data collected by the server, and the output is summarized information. Specifically, the server inputs data into the natural language processing model and obtains the summarization result.

[1435] Step 4:

[1436] The server organizes the summarized data into categories. The input to this process is summarized information, and the output is data organized by category. Specifically, the server classifies the summarized data into categories such as "work information," "maintenance information," and "equipment usage information."

[1437] Step 5:

[1438] The server displays the organized data as a user interface. The input to this process is data organized by category, and the output is a dashboard-style interface. Specifically, the server sends the organized data in an appropriate format to terminals, smart glasses, or head-mounted displays.

[1439] Step 6:

[1440] Users view organized information through the provided user interface. The input to this process is a dashboard-style interface, and the output is the user's understanding and use of the information. Specifically, users use a device or smart glasses to view the provided information and take necessary actions.

[1441] 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.

[1442] This invention relates to a system that automatically collects, summarizes, and organizes various user business data and provides it as handover information, in addition to a system that combines this with an emotion engine that recognizes the user's emotions.

[1443] System Configuration

[1444] This system includes the following main components:

[1445] 1. Data acquisition methods

[1446] 2. Data summarization means

[1447] 3. Data organization methods

[1448] 4. Means of providing a user interface

[1449] 5. Emotional Engine

[1450] Specific behavior of each component

[1451] Data acquisition methods

[1452] The server first verifies the user's authentication information and obtains permission to access the data. If authentication is successful, the server collects email data, project management data, and document management data from their respective systems.

[1453] As a concrete example, this involves retrieving email data from a user's email server (e.g., Gmail) via an API, and similarly collecting project data from project management tools (e.g., Trello or Asana). It also involves collecting document data from document management systems (e.g., Google Drive or Dropbox).

[1454] Data summarization means

[1455] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information.

[1456] For example, if there is a large amount of text data regarding the progress of a project, the most important points and progress are extracted from that text and summarized briefly.

[1457] Data organization methods

[1458] The server also includes a means of organizing the summarized data by category. Specifically, email-related information is classified and organized into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[1459] Emotional Engine

[1460] The server analyzes the user's emotions using an emotion engine. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the user's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[1461] Data summarization and integration of sentiment data

[1462] The server integrates the summarized data with the recognized sentiment data. This generates handover information that reflects the user's emotional state. For example, if the user's emotions in a particular project are negative, that information is also included in the summarized data.

[1463] User interface provisioning means

[1464] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitively operable dashboard. This allows users to easily check handover information and simultaneously understand fluctuations in their emotions.

[1465] Specific example

[1466] Example 1: Data collection and sentiment recognition

[1467] When User A is being transferred and User B is taking over their duties, the server first uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, collects ongoing project data from the project management tool, and gathers all relevant documents from the document management system. Furthermore, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[1468] Example 2: Data summarization and sentiment reflection

[1469] The server analyzes the collected data using natural language processing technology, extracts key points, and summarizes them into categories. For example, it extracts and summarizes information about the achievement status of important milestones in a particular project and the division of roles among key members. Simultaneously, sentiment data is analyzed by an emotion engine, and this information is included alongside the summarized data.

[1470] Example 3: Provision to users

[1471] Finally, this summary data and sentiment data are organized and presented as a user interface. User B can view a dashboard provided by the server through their device, allowing them to grasp at a glance the email summary, project progress, related document overview, and User A's sentiment information.

[1472] As described above, the present invention automates information gathering, summarization, and organization during business handover, and also takes into account the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[1473] The following describes the processing flow.

[1474] Step 1:

[1475] The server verifies the user's authentication credentials. Using these credentials, it confirms that the user has the necessary access rights and obtains permission to access the data.

[1476] Step 2:

[1477] The server collects email data. This is done by retrieving email data from the mail server via an API and collecting the user's sending and receiving history.

[1478] Step 3:

[1479] The server collects project management data. It retrieves data about user tasks and project progress from project management tools (e.g., Trello and Asana) via APIs.

[1480] Step 4:

[1481] The server collects document management data. It collects document data saved by users from document management systems (e.g., Google Drive or Dropbox) via APIs.

[1482] Step 5:

[1483] The server integrates collected email data, project management data, and document management data, combining all the information into a single dataset.

[1484] Step 6:

[1485] The server performs natural language processing (NLP) on the integrated data. Using advanced NLP tools such as the BERT model, it analyzes and summarizes the important information from each data point.

[1486] Step 7:

[1487] The server uses an emotion engine to analyze the user's emotions. It identifies emotions such as positive, negative, and neutral from the wording and content contained in email data and project management data.

[1488] Step 8:

[1489] The server organizes the summarized data into categories. Specifically, email-related information is categorized as "Email," project progress-related information as "Project," and document-related information as "Document." Furthermore, perceived sentiment information is added to each piece of data.

[1490] Step 9:

[1491] The server formats organized data and sentiment data into datasets for delivery as a user interface. It converts them into a format that is easily accessible to users.

[1492] Step 10:

[1493] The terminal receives organized data from the server and displays it to the user as a dashboard. The data is provided in a way that is easy for the user to understand intuitively.

[1494] Step 11:

[1495] Users interact with the dashboard displayed on their device to confirm necessary handover information. They can view summarized information for each category, along with recognized sentiment information, to help ensure the continuity of their work.

[1496] Step 12:

[1497] Users refer to the displayed sentiment data to understand the difficulty level and points to note regarding the handover process. For example, they will pay particular attention to projects where many negative emotions are recognized, and take steps to identify problems and implement countermeasures.

[1498] (Example 2)

[1499] 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".

[1500] Conventional business handover systems struggled to efficiently collect, summarize, and organize user business data. Furthermore, they failed to provide information that took user emotions into consideration, leaving challenges in business continuity and efficiency. This resulted in a lack of accuracy and comprehensiveness in handover information, hindering a smooth transition of operations.

[1501] 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.

[1502] In this invention, the server includes means for collecting user email data, means for collecting user project management data, means for collecting user document management data, means for analyzing and summarizing each collected data using natural language processing, means for organizing the summarized data by category, means for analyzing the collected data using an emotion engine that recognizes user emotions, means for integrating the summarized data and recognized emotion data, and means for displaying the organized data and emotion data as a user interface. This makes it possible to efficiently collect, summarize, and organize user business data, and to provide comprehensive handover information with added emotion information.

[1503] A "user" is an individual or organization that uses the system.

[1504] "Email data" refers to all information about emails sent and received by a user through their email account.

[1505] "Project management data" refers to information such as project progress, tasks, and resources, obtained from systems used by users to manage projects.

[1506] "Document management data" refers to information about documents and files obtained from systems used by users to manage their documents.

[1507] "Natural language processing" is a technology that uses computers to understand, analyze, and generate human language.

[1508] "Methods of summarization" refer to the process of extracting essential information from collected data and compressing it into a concise format for presentation.

[1509] "Methods of organizing by category" refer to the process of grouping and organizing collected and summarized data according to specific classification criteria.

[1510] An "emotion engine" is an algorithm or system used to analyze and identify emotions from a user's text data.

[1511] "Integration" refers to the process of combining summary data and sentiment data and providing them in a user-friendly format.

[1512] A "user interface" is the visual and operational structure that allows a user to access and operate a system.

[1513] This invention relates to a system that automatically collects, summarizes, and organizes user business data and provides it as handover information. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to provide more comprehensive handover information.

[1514] Data acquisition methods

[1515] The server first verifies the user's authentication information and obtains permission to access the data. Specifically, the server receives the username and password and compares them with the authentication information stored in the database. If authentication is successful, the server uses APIs (Application Programming Interfaces) to retrieve the user's email data, project management data, and document management data from their respective systems. For example, it might use the Gmail API to collect email data, retrieve project data from project management tools such as Trello and Asana, and retrieve document data from document management systems such as Google Drive and Dropbox.

[1516] Data summarization means

[1517] The server analyzes the collected data using natural language processing (NLP) techniques. Specifically, it uses advanced NLP models such as the BERT model to extract and summarize important information. For example, if there is a large amount of text data about project progress, it will extract the most important points and progress from the text and summarize them concisely.

[1518] Data organization methods

[1519] The server organizes the summarized data by category. Specifically, it classifies and organizes email-related information into the "Email" category, project progress-related information into the "Project" category, and document-related information into the "Document" category.

[1520] Emotional Engine

[1521] The server uses an emotion engine to analyze the user's emotions. The emotion engine recognizes the user's emotions based on collected email data, project management data, and document management data. Using an emotion recognition algorithm, it extracts the emotions contained in the user's messages and tasks and classifies them into emotion categories such as positive, negative, and neutral.

[1522] Data summarization and integration of sentiment data

[1523] The server integrates the summarized data with the recognized sentiment data. For example, if a user's sentiment is negative in relation to project progress information, that sentiment information is also added to the summarized data.

[1524] User interface provisioning means

[1525] The device receives organized data and sentiment data provided by the server and displays it as an intuitive dashboard. This allows users to easily check handover information and sentiment data.

[1526] Specific example

[1527] Example 1: Data collection and sentiment recognition

[1528] When User A is transferring to another department and User B is taking over their duties, the server uses User A's authentication credentials to access each data source. It retrieves all email data from the mail server for the past six months, current project data from the project management tool, and all relevant documents from the document management system. Next, it uses an emotion engine to recognize the emotions contained in email messages and project tasks and reflects them in the data.

[1529] Example 2: Data summarization and sentiment reflection

[1530] The server analyzes the collected data using natural language processing technology, extracting and summarizing key points. For example, it analyzes and extracts the achievement status of important milestones in a specific project and the division of roles among key members. Simultaneously, sentiment data analyzed by the sentiment engine is added to the summarized data.

[1531] Example 3: Provision to users

[1532] These summary and sentiment data are integrated on the server and transferred to the terminal. User B can view the dashboard provided by the server through the terminal, and get an overview of the email summary, project progress, related document summaries, and User A's sentiment information at a glance.

[1533] Example of a prompt

[1534] Extract the key points from the following information and summarize them in a bulleted list. Also, categorize and present the sentiment data.

[1535] 1. Project progress report

[1536] 2. Large amounts of email data

[1537] 3. Data from the document management system

[1538] As described above, the present invention automates the collection, summarization, and organization of information during business handover, and further incorporates the user's emotions, thereby supporting more efficient and comprehensive business continuity.

[1539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1540] Step 1:

[1541] The server verifies the user's authentication information.

[1542] Input: Username, Password

[1543] Data processing and calculation: The server authenticates the user by comparing the username and password with the information stored in the database.

[1544] Output: Authentication success or failure status

[1545] Specific operation: When a user enters their username and password on the login screen and clicks the "Login" button, the server starts the authentication process. If authentication is successful, a session is started and the process proceeds to the next data collection process.

[1546] Step 2:

[1547] The server collects email data, project management data, and document management data from each system for users who have successfully authenticated.

[1548] Input: Authentication success status, access information for each data source

[1549] Data processing and calculation: The server retrieves data from each system using APIs. For example, it collects email data using the Gmail API, project data using the Trello API, and document data using the Google Drive API.

[1550] Output: Collected datasets (email data, project management data, document management data)

[1551] Specific operation: The server accesses the endpoint of each API and sends a request to retrieve data. If the data is successfully retrieved, it is temporarily stored in the server's database.

[1552] Step 3:

[1553] The server analyzes and summarizes the collected data using natural language processing (NLP) techniques.

[1554] Input: Collected dataset

[1555] Data processing and computation: We use advanced NLP models such as the BERT model to extract important information and summarize it concisely.

[1556] Output: Summarized dataset (email summaries, project summaries, document summaries)

[1557] Specific operation: The NLP engine on the server analyzes the collected data and performs processes such as "summarizing the content of an email in five lines or less."

[1558] Step 4:

[1559] The server organizes the summarized data by category.

[1560] Input: Summarized dataset

[1561] Data processing and calculation: The server classifies data based on data categories ("email," "project," "document," etc.).

[1562] Output: Datasets organized by category

[1563] Specific operation: The server assigns metadata tags (category information) to the summary data and organizes and stores it in the database for each category.

[1564] Step 5:

[1565] The server uses an emotion engine to analyze the collected data and recognize the user's emotions.

[1566] Input: Collected dataset

[1567] Data Processing and Calculation: Using emotion recognition algorithms, extract and classify positive, negative, and neutral emotions from text data.

[1568] Output: Sentiment dataset (sentiment information for emails, projects, and documents)

[1569] Specific operation: The emotion engine analyzes each piece of text data and adds emotion tags to the data, such as "the content of the email is positive."

[1570] Step 6:

[1571] The server integrates summary data and sentiment data.

[1572] Input: Summarized dataset, sentiment dataset

[1573] Data processing and computation: Integrate sentiment information into summarized data to generate a unified dataset.

[1574] Output: Integrated dataset

[1575] Specific operation: The server matches sentiment data with summary data to generate integrated data, such as "Project progress is on track, but user sentiment is negative."

[1576] Step 7:

[1577] The device receives organized data and sentiment data provided by the server and displays it as a dashboard.

[1578] Input: Integrated dataset

[1579] Data processing / calculations: None (only processing for display purposes)

[1580] Output: Dashboard displayed as the user interface

[1581] Specific operation: The terminal receives data from the server and displays it to the user as a dashboard. This dashboard visually displays email summaries, project progress, summaries of related documents, and user sentiment information.

[1582] (Application Example 2)

[1583] 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".

[1584] In factories and workplaces, there are challenges in automatically collecting, organizing, and summarizing data, as well as accurately understanding the emotional state of workers. There is a need for a method to efficiently manage on-site work data and monitor workers' stress levels and motivation in real time.

[1585] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting the user's electronic communication data, means for collecting the user's task management data, means for collecting the user's file management data, means for analyzing and summarizing each of the collected data by natural language processing, means for organizing the summarized data by category, means for recognizing the worker's emotional state based on the organized data, means for integrating and displaying the summarized data and the emotional state, and means for displaying the integrated data as a user interface. This enables efficient management of work data at the site and appropriate understanding of the worker's emotional state.

[1586] "User electronic communication data" refers to information exchanged via email and messaging platforms.

[1587] "User task management data" refers to information collected from project management tools, task lists, and scheduling tools.

[1588] "User file management data" refers to information about electronic files that users save and manage, such as documents, spreadsheets, and presentations.

[1589] "Natural language processing" refers to the technology that enables computers to understand, generate, and analyze human language.

[1590] "Methods of summarization" refer to methods of extracting important information using natural language processing techniques and summarizing it concisely.

[1591] "Organizing by category" refers to a method of classifying collected information into specific groups or categories and managing it efficiently.

[1592] "Means of recognizing emotional states" refers to technologies that analyze and judge an individual's emotions using facial recognition technology or voice analysis.

[1593] "Means of integrated display" refers to methods and technologies for combining summary data and sentiment data and displaying them in a single interface.

[1594] "Means of displaying as a user interface" refers to technologies and methods that visually provide information in a format that is easy for users to operate.

[1595] This invention aims to construct a system for the efficient management of work data in factories and workplaces, and for the appropriate understanding of workers' emotional states. The specific implementation method of the system is described below.

[1596] System Configuration

[1597] This system includes the following main components:

[1598] 1. Data acquisition methods

[1599] 2. Data summarization means

[1600] 3. Data organization methods

[1601] 4. Emotional Engine

[1602] 5. Data Integration Means

[1603] 6. Means of providing a user interface

[1604] Data acquisition methods

[1605] The server first verifies the on-site authentication information and obtains permission to access the data. The data collected includes electronic communication data (e.g., emails and messages), task management data (e.g., information from project management tools), and file management data (e.g., documents and spreadsheets).

[1606] Data summarization means

[1607] The server analyzes the collected data using natural language processing (NLP) techniques. In this process, advanced NLP models, such as the BERT model, are used to extract and summarize important information. For example, key points such as working hours, work speed, and success rate are extracted from work logs and sensor data.

[1608] Data organization methods

[1609] The server also includes a means of organizing summarized data into categories. For example, information related to electronic communication is classified and organized into the "Communication" category, information related to task management into the "Tasks" category, and information related to file management into the "Files" category.

[1610] Emotional Engine

[1611] The server uses an emotion engine to analyze the worker's emotional state. The emotion engine recognizes the worker's emotions based on collected files, tasks, and electronic communication data. In this process, an emotion recognition algorithm is used to extract the emotions contained in the worker's messages and tasks and classify them into emotion categories such as positive, negative, and neutral.

[1612] Data Integration Means

[1613] The server integrates the summarized data with the recognized sentiment data. This generates work information that reflects the worker's emotional state. For example, if a worker's emotions are negative during a particular task, that information is also included in the summarized data.

[1614] User interface provisioning means

[1615] Organized data and sentiment data are provided as a user interface. The terminal receives data retrieved from the server and displays it as an intuitive dashboard. This allows work managers to easily check work information and the emotional state of workers.

[1616] Specific hardware and software to be used

[1617] Hardware: Servers, sensors, cameras

[1618] Software: Python, NLP models (BERT model), sentiment recognition algorithms, project management tools

[1619] Data Processing & Data Calculation: Natural Language Processing, Sentiment Analysis, Data Integration and Display

[1620] Specific example

[1621] To determine whether a worker's emotional state is affecting work efficiency, this system operates as follows:

[1622] As a concrete example, you can enter the following prompt:

[1623] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[1624] This will result in the following output:

[1625] Summary of yesterday's assembly line work:

[1626] Tasks: Packaging, labeling

[1627] Completion Status: Packaging - Completed, Labeling - In Progress

[1628] Total working time: 8 hours

[1629] Employee emotional state:

[1630] Packaging: Positive

[1631] Labeling: Negative

[1632] Thus, the present invention supports the evaluation of overall work efficiency and the work environment, from the collection of work data to the analysis of emotional data and the display of integrated data.

[1633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1634] Step 1:

[1635] The server verifies the authentication information at the site and obtains permission to access the data. In this step, the user's authentication information is taken as input, and permission to collect data is taken as output. Specifically, it communicates with the authentication server and obtains an authentication token.

[1636] Step 2:

[1637] The server collects electronic communication data, task management data, and file management data from their respective systems. In this step, an authentication token is taken as input, and the collected dataset is taken as output. Specifically, API requests are sent to each data source to retrieve the data.

[1638] Step 3:

[1639] The server analyzes and summarizes each collected data using natural language processing techniques. In this step, the collected data is taken as input, and the summarized data is output. Specifically, the BERT model is used to extract important information and perform text summarization.

[1640] Step 4:

[1641] The server organizes the summarized data into categories. In this step, the summarized data is taken as input and the categorized data is output. Specifically, a semantic classification algorithm is used to separate the data into categories.

[1642] Step 5:

[1643] The server uses an emotion engine to recognize the worker's emotional state. In this step, organized data is taken as input and emotional state data is output. Specifically, facial recognition technology and voice analysis are used to analyze emotions and classify them as positive, negative, neutral, etc.

[1644] Step 6:

[1645] The server integrates the summarized data with the recognized sentiment data. In this step, categorical data and sentiment state data are taken as input, and the integrated data is output. Specifically, the two sets of data are mapped and combined into a single data format.

[1646] Step 7:

[1647] The terminal displays the integrated data retrieved from the server as an intuitive dashboard. In this step, the integrated data is taken as input, and the visual display as a dashboard is taken as output. Specifically, this involves generating a GUI and visualizing the data using a web framework.

[1648] Example prompt statements

[1649] "Please display a summary of yesterday's assembly line work, along with the employees' emotional state."

[1650] The above is a detailed explanation of the processing steps in the example program.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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."

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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 to be incorporated by reference.

[1672] The following is further disclosed regarding the embodiments described above.

[1673] (Claim 1)

[1674] Means for collecting user email data,

[1675] A means of collecting user project management data,

[1676] A means of collecting user document management data,

[1677] A means of analyzing and summarizing each collected data using natural language processing,

[1678] A means of organizing summarized data by category,

[1679] A system that includes means for displaying organized data as a user interface.

[1680] (Claim 2)

[1681] The system according to claim 1, further comprising means for verifying authentication information and obtaining permission for the user to access data.

[1682] (Claim 3)

[1683] The system according to claim 1, comprising means of using a predetermined communication protocol in the collection of each data.

[1684] "Example 1"

[1685] Claims

[1686] (Claim 1)

[1687] Means for collecting user email data,

[1688] A means of collecting user project management data,

[1689] A means of collecting user document management data,

[1690] A means of analyzing and summarizing each collected data using natural language processing,

[1691] A means of organizing summarized data by category,

[1692] A means of verifying authentication information and obtaining permission for the user to access data,

[1693] A system that includes a means for specifying data categories and displaying organized data as a user interface.

[1694] (Claim 2)

[1695] The system according to claim 1, further comprising means for analyzing collected data using a generation AI model and generating prompt sentences.

[1696] (Claim 3)

[1697] The system according to claim 1, comprising means of using a predetermined communication protocol in the collection of each data.

[1698] "Application Example 1"

[1699] (Claim 1)

[1700] Means of collecting user data,

[1701] Means for collecting user work data,

[1702] Means for collecting user maintenance data,

[1703] Means for collecting user document data,

[1704] A means of analyzing and summarizing each collected data using natural language processing,

[1705] A means of organizing summarized data by category,

[1706] A system that includes means for displaying organized data as an intuitively operable user interface.

[1707] (Claim 2)

[1708] The system according to claim 1, further comprising means for verifying user authentication information and obtaining permission for data access.

[1709] (Claim 3)

[1710] The system according to claim 1, further comprising means of using a predetermined communication protocol in the collection of each data.

[1711] "Example 2 of combining an emotion engine"

[1712] (Claim 1)

[1713] Means for collecting user email data,

[1714] A means of collecting user project management data,

[1715] A means of collecting user document management data,

[1716] A means of analyzing and summarizing each collected data using natural language processing,

[1717] A means of organizing summarized data by category,

[1718] A means of analyzing data collected using an emotion engine that recognizes user emotions,

[1719] A means of integrating summarized data and perceived sentiment data,

[1720] A means for displaying organized data and sentiment data as a user interface,

[1721] A system that includes this.

[1722] (Claim 2)

[1723] The system according to claim 1, further comprising means for verifying authentication information and obtaining permission for the user to access data.

[1724] (Claim 3)

[1725] The system according to claim 1, comprising means of using a predetermined communication protocol in the collection of each data.

[1726] "Application example 2 when combining with an emotional engine"

[1727] (Claim 1)

[1728] Means for collecting users' electronic communication data,

[1729] A means of collecting user task management data,

[1730] A means of collecting user file management data,

[1731] A means of analyzing and summarizing each collected data using natural language processing,

[1732] A means of organizing summarized data by category,

[1733] A means of recognizing the emotional state of workers based on organized data,

[1734] A means of integrating and displaying summarized data and emotional states,

[1735] A system that includes means for displaying integrated data as a user interface.

[1736] (Claim 2)

[1737] The system according to claim 1, further comprising means for verifying authentication information and obtaining permission for the user to access data.

[1738] (Claim 3)

[1739] The system according to claim 1, which includes means for using a predetermined communication protocol in the collection of each data. [Explanation of Symbols]

[1740] 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. Means for collecting user email data, Means for collecting user project management data, A means of collecting user document management data, A means of analyzing and summarizing each collected data using natural language processing, A means of organizing summarized data by category, A system that includes means for displaying organized data as a user interface.

2. The system according to claim 1, further comprising means for verifying authentication information and obtaining permission for the user to access data.

3. The system according to claim 1, comprising means of using a predetermined communication protocol in the collection of each data.

Images