system

Abstract

We provide the system. [Solution] A means for collecting opinions entered by users, Means for transmitting the aforementioned opinion to a central processing unit, The central processing unit includes means for classifying the opinions by natural language processing, A means of extracting issues and proposals from classified opinions and generating an agenda, A means for sending the aforementioned agenda to the user's terminal and notifying them, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] In the conventional breast, the classification of opinions depends on human subjective and arbitrary judgments, and there is a problem that it is difficult to classify opinions fairly and efficiently. Also, the work of extracting important issues and proposals from a huge number of opinions and summarizing them as an agenda is complicated and requires time and labor. For this reason, the results of the breast cannot be effectively utilized, and the problem is that it does not contribute to improving the productivity of the meeting.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a system including means for collecting user inputs and means for transmitting the collected opinions to a central processing unit. The central processing unit has means for classifying the received opinions using natural language processing, extracts key issues and proposals from the classified opinions, and generates an agenda. Furthermore, it includes means for transmitting the generated agenda to the user's terminal and notifying them. This enables fair and efficient classification of opinions in brainstorming, and allows for the rapid extraction of important issues and proposals and their compilation into an agenda. In addition, by providing means for the central processing unit to store opinions in a database, it becomes possible to manage and refer to past opinions and agendas.

[0006] A "user" refers to an individual or organization that uses a system to input opinions and receives the results.

[0007] "Opinions" refer to text information about various suggestions, issues, and observations that users input during the brainstorming process.

[0008] "Means of collection" refers to interfaces and mechanisms for compiling and managing opinions entered by users.

[0009] A "central processing unit" is a computer system, such as a server, that includes hardware and software for processing and analyzing collected opinions.

[0010] "Means of transmission" refers to data communication methods and protocols for transferring collected opinions to a central processing unit.

[0011] "Natural language processing" is a general term for technologies that enable computers to understand, analyze, and generate human language.

[0012] "Classification methods" refer to algorithms and modules that use natural language processing to divide opinions into topics or categories.

[0013] "Issues" refer to problems raised by users or pending issues that need to be resolved.

[0014] A "suggestion" refers to a solution or improvement proposed by a user.

[0015] An "agenda" is a document or list that summarizes the main issues and proposals extracted from categorized opinions.

[0016] "Means of sending and notifying" refers to communication protocols and methods for transferring the generated agenda to the user's device and informing the user.

[0017] A "database" is an information management system that continuously stores saved opinions, classification results, generated agendas, and other information, making them accessible when needed.

[0018] "Means of management and referencing" refer to interfaces and functions for searching, extracting, and displaying information within a database. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an 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 an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] 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.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system provides the following functions:

[0041] Gathering opinions

[0042] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[0043] Receiving and saving feedback

[0044] The server has an API endpoint for receiving feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[0045] Classification of opinions

[0046] The server classifies the received opinions using natural language processing (NLP). This processing is performed using generative AI or other appropriate algorithms. The purpose of classification is to divide the opinions into topics or categories. For example, the opinion "the project is progressing slowly" would be classified under the category "project management." The classification results are stored in a database.

[0047] Agenda generation

[0048] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. The generated agenda is stored in a database and formatted in a user-friendly format.

[0049] Agenda notification

[0050] The server sends the generated agenda to the user's device and notifies them. By referring to the agenda, the user can effectively utilize the results of the brainstorming session. When the notification is sent, information about the agenda will be displayed on the user's device via a pop-up message or email.

[0051] Specific example

[0052] For example, a user enters the following comment:

[0053] 1. "The project is progressing slowly."

[0054] 2. "There is a lack of communication."

[0055] 3. "We should introduce new tools."

[0056] 4. "The priorities of the tasks are unclear."

[0057] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[0058] Category 1: "Project Management"

[0059] Issues: "Slow progress," "Unclear priorities"

[0060] Proposal: "Introduction of new tools"

[0061] Category 2: "Communication"

[0062] Problem: "Lack of communication"

[0063] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[0064] Category 1: "Project Management"

[0065] assignment:

[0066] Slow progress

[0067] Priorities are unclear

[0068] suggestion:

[0069] Introducing new tools

[0070] Category 2: "Communication"

[0071] assignment:

[0072] There is a lack of communication.

[0073] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0074] As described above, the present invention is a system that can fairly and efficiently classify opinions from brainstorming sessions and compile important issues and proposals into an agenda.

[0075] The following describes the processing flow.

[0076] Program processing flow

[0077] Step 1: Enter and submit your comments.

[0078] Terminal:

[0079] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[0080] Specific actions:

[0081] 1. The user enters their opinion in the text box.

[0082] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[0083] Step 2: Receiving and saving feedback

[0084] server:

[0085] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[0086] Specific actions:

[0087] 1. The server receives a request to submit feedback from the terminal.

[0088] 2. Save the received feedback to the database.

[0089] 3. After saving is complete, a "Received" message will be sent to the device.

[0090] Step 3: Classification of Opinions

[0091] server:

[0092] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[0093] Specific actions:

[0094] 1. The server reads unclassified comments from the database.

[0095] 2. Pass the opinions to the NLP module and obtain the classification results.

[0096] 3. Re-save the categorized opinions in the database.

[0097] Step 4: Agenda Generation

[0098] server:

[0099] The server extracts key issues and proposals from the categorized opinions and generates an agenda. The agenda organizes the issues and proposals for each topic.

[0100] Specific actions:

[0101] 1. The server retrieves pre-classified opinions from the database.

[0102] 2. Identify the main issues and proposals for each category.

[0103] 3. Summarize the issues and proposals to create an agenda.

[0104] 4. Save the agenda to the database.

[0105] Step 5: Agenda Notification

[0106] server:

[0107] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0108] Specific actions:

[0109] 1. The server reads the generated agenda from the database.

[0110] 2. Prepare the agenda in a format suitable for notifying users.

[0111] 3. Send the agenda to the user's device and notify them.

[0112] Step 6: User Verification

[0113] User:

[0114] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[0115] Specific actions:

[0116] 1. The user receives a notification and checks the agenda.

[0117] 2. Based on the agenda, formulate necessary plans and measures.

[0118] Thus, the system of the present invention is carried out in a series of steps, starting with user input of opinions, followed by saving, classifying, generating an agenda, notification, and user confirmation.

[0119] (Example 1)

[0120] 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."

[0121] In traditional brainstorming sessions, it was difficult to efficiently collect, classify, and organize the opinions entered by users. Furthermore, there was no method for quickly and accurately generating an agenda summarizing key issues and proposals. This resulted in problems such as insufficient organization of opinions and the inability to create effective agendas.

[0122] 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.

[0123] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, and means for transmitting and notifying the user of the agenda to the user's terminal. This makes it possible to effectively collect, classify, and organize opinions submitted by users in brainstorming sessions, and to quickly compile important issues and proposals into an agenda.

[0124] A "user" is someone who uses the system to input opinions and participate in brainstorming sessions.

[0125] "Opinions" refer to suggestions, issues, and information and feedback that users input during a brainstorming session.

[0126] "Means of collection" refers to mechanisms or devices for collecting and acquiring opinions entered by users.

[0127] "Transmission means" refers to methods or devices for sending collected opinions to a central processing unit.

[0128] A "central processing unit" is a computer system or server that receives opinions sent by users and processes them accordingly.

[0129] "Natural language processing" is a field of computer science that aims to mechanically analyze opinions and understand their meaning and intent.

[0130] A "classification method" refers to a method or device that uses natural language processing to organize received opinions into topics or categories.

[0131] "Extraction methods" refer to methods or devices for identifying and extracting important issues and proposals from classified opinions.

[0132] An "agenda generation method" refers to a method or device for organizing extracted issues and proposals and compiling them into an agenda.

[0133] "Notification means" refers to a method or device for sending the generated agenda to the user's terminal to inform them.

[0134] A "device" refers to a computer or mobile device that a user uses to input opinions and receive notifications.

[0135] A "database" is a storage location or system for saving and managing information such as received opinions, classification results, and agendas.

[0136] "Means of formatting" refers to methods or devices for arranging an agenda into a format that is easy for users to see and understand.

[0137] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system is implemented using the following hardware and software.

[0138] Users enter their opinions using a dedicated UI form on their device. For example, a web form built with HTML and CSS might be used, with JavaScript® triggering a click event for the submit button. This form might include text boxes and a submit button.

[0139] The terminal sends the inputted opinion to the server. The server receives the request via an API endpoint (e.g., a REST API). The server uses a Python framework (such as Flask or Django) to set up the API endpoint, and upon receiving the opinion, it saves the opinion to a database (such as MySQL or PostgreSQL) using a Python ORM (Object-Relational Mapping). If the saving of the opinion is successful, the server returns a "Received" message to the terminal.

[0140] The server classifies the stored opinions using natural language processing (NLP). This analysis uses a generative AI model (e.g., GPT-3®) or other appropriate algorithms. For example, a Python script is used to retrieve opinion data from the database and generate prompt sentences to input into the AI ​​model. The AI ​​model returns the classification results, which are then stored in the database.

[0141] Next, the server extracts key issues and suggestions from the categorized opinions and generates an agenda. In this process, the main issues and suggestions are organized within each category. The generated agenda is then formatted in a user-friendly format, for example, using a formatting library such as Markdown.

[0142] The server sends the generated agenda to the user's device and notifies them. Notifications are sent via pop-up messages or email. For example, notifications can be sent to the device using a REST API or WebSocket, and email notifications use the SMTP protocol. Users refer to the notified agenda and effectively utilize the results of the brainstorming session.

[0143] Specific example

[0144] For example, suppose a user enters feedback such as "Project progress is slow," "There is a lack of communication," "A new tool should be introduced," or "The work priorities are unclear" into a terminal and presses the send button. The terminal sends these feedback to a server, which stores them in a database. The server then uses natural language processing to classify the stored feedback as follows:

[0145] Category 1: "Project Management"

[0146] Issues: "Slow progress," "Unclear priorities"

[0147] Proposal: "Introduction of new tools"

[0148] Category 2: "Communication"

[0149] Problem: "Lack of communication"

[0150] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[0151] Category 1: "Project Management"

[0152] assignment:

[0153] Slow progress

[0154] Priorities are unclear

[0155] suggestion:

[0156] Introducing new tools

[0157] Category 2: "Communication"

[0158] assignment:

[0159] There is a lack of communication.

[0160] Examples of prompt statements

[0161] The following are specific examples of prompt statements to be input into a generative AI model:

[0162] The following are the opinions submitted by users during the brainstorming session.

[0163] 1. The project is progressing slowly.

[0164] 2. Lack of communication

[0165] 3. New tools should be introduced.

[0166] 4. The priorities of the tasks are unclear.

[0167] Please categorize these opinions and list the issues and suggestions for each category.

[0168] The present invention is a system that can effectively collect, classify, and organize the opinions submitted by users as described above, and quickly compile important issues and proposals into an agenda.

[0169] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0170] Step 1:

[0171] The user enters their opinion. The user uses a dedicated UI form on their device (built with HTML and CSS) to enter their opinion. For example, they might enter "Project progress is slow" and click the "Submit" button. The input data is in text format.

[0172] Specific actions:

[0173] The device uses JavaScript to detect the click event of the submit button and prepares the feedback as submission data.

[0174] Step 2:

[0175] The terminal sends the inputted opinion to the server. The terminal sends the opinion data as an HTTP request to the server's API endpoint.

[0176] Input: User-submitted opinions

[0177] Output: Opinion data sent to the server

[0178] Specific actions:

[0179] The device sends feedback to the server using JavaScript's Fetch API or XMLHttpRequest.

[0180] Step 3:

[0181] The server receives the feedback and saves it to a database. The server receives feedback data via an API endpoint (for example, using the Flask or Django framework in Python) and saves it to a database (MySQL or PostgreSQL). If the save is successful, the server returns a "received" message to the terminal.

[0182] Input: Opinion data sent from the device

[0183] Output: Opinion data stored in the database, "Received" message

[0184] Specific actions:

[0185] The server receives opinion data using an API built with Flask or Django and stores it in a database using an ORM.

[0186] Step 4:

[0187] The server analyzes and classifies the stored opinions using natural language processing (NLP). The server generates prompt sentences to input the stored opinion data into a generating AI model (e.g., GPT-3). The model classifies the opinions into categories (e.g., "project management" or "communication") and returns the results. The classification results are stored in a database.

[0188] Input: Opinion data stored in the database

[0189] Output: Classified opinion data, classification results

[0190] Specific actions:

[0191] The server uses a Python script to acquire opinion data and create prompt statements to input into the generative AI model.

[0192] The classification results obtained from the AI ​​model are stored in a database.

[0193] Step 5:

[0194] The server extracts key issues and suggestions from categorized opinions and generates an agenda. It organizes the main issues and suggestions by category and creates the agenda using a formatting library such as Markdown. This agenda is then stored in a database.

[0195] Input: Classified opinion data

[0196] Output: Generated agenda

[0197] Specific actions:

[0198] The server uses a Python script to process the classification results and extract issues and suggestions.

[0199] Format the agenda using a formatting library and save it to the database.

[0200] Step 6:

[0201] The server sends the generated agenda to the user's device and notifies them. This notification is delivered via a pop-up message or email. This allows the user to view the agenda.

[0202] Input: Generated agenda

[0203] Output: Agenda notification displayed on the user's device.

[0204] Specific actions:

[0205] The server sends the agenda to the terminal using a REST API or WebSocket.

[0206] For email notifications, emails are sent using SMTP.

[0207] The above processing steps enable the effective collection, classification, and organization of user input, allowing for the rapid compilation of important issues and suggestions into an agenda.

[0208] (Application Example 1)

[0209] 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."

[0210] In industrial environments, there is a need for methods to effectively collect worker opinions and improvement suggestions, appropriately classify and organize them, and summarize important issues and proposals. Furthermore, an efficient system is required to quickly notify managers of the collected opinions and implement them as improvement plans.

[0211] 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.

[0212] In this invention, the server includes means for collecting user inputs, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting the agenda to the user's terminal and notifying them, means for industrial workers to input their opinions into an interface, and means for collecting opinions via factory robots and transmitting them to the server. This makes it possible to effectively collect opinions from workers, quickly classify and organize them, and notify administrators.

[0213] (Definition of terms)

[0214] A "user" is a person, such as a worker or manager, who uses this system to input opinions or suggestions.

[0215] "Feedback" refers to improvement suggestions, reports, and comments on issues entered by users.

[0216] "Means of collection" refer to devices such as interfaces and sensors that electronically capture opinions entered by users.

[0217] "Means of transmission" refers to the process or device for sending collected opinions to a central processing unit via a communication network.

[0218] A "central processing unit" is a computer system or server used to process incoming opinions.

[0219] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0220] "Classification methods" refer to processes or devices that divide opinions collected using natural language processing into specific categories or topics.

[0221] A "problem" is a problem raised by a user that requires a solution.

[0222] A "proposal" is a solution or improvement suggested by a user to address a problem.

[0223] An "agenda" is a list that organizes the issues and proposals extracted from categorized opinions.

[0224] "Means of notification" refers to processes or devices for informing users' devices of the generated agenda.

[0225] "Industrial environment workers" are employees who perform work in industrial facilities such as factories.

[0226] An "interface" refers to a device or software used by workers to input their opinions.

[0227] A "factory robot" is an automated device that performs tasks automatically within a factory and collects feedback.

[0228] A "database" is an information storage system for saving and managing received opinions and generated agendas.

[0229] overview

[0230] This invention is a system that collects opinions and suggestions from workers in an industrial environment, classifies them using natural language processing (NLP), extracts and organizes important issues and helpful suggestions, and generates an agenda. The generated agenda is notified to the terminals of workers and managers and used for rapid decision-making and improvement activities.

[0231] Hardware and software to be used

[0232] 1. Hardware

[0233] Smartphone: An interface for workers to input their opinions.

[0234] Factory robots: These robots have the function of collecting opinions from the industrial environment and sending them to a server.

[0235] Server: A central processing unit that receives, classifies, and generates agendas for opinions.

[0236] 2. Software

[0237] Python: A programming language used to write the main parts of a program.

[0238] Flask: A web framework for building API servers.

[0239] Spacy: A library for performing natural language processing (NLP).

[0240] SQLite: A database management system.

[0241] Processing flow

[0242] 1. Gathering opinions

[0243] Workers input their opinions using a smartphone or an interface mounted on a factory robot. The interface includes text boxes and a submit button.

[0244] The worker enters their opinion and presses the submit button, which sends the opinion to the server.

[0245] 2. Receiving and storing feedback

[0246] The server receives feedback via an API endpoint built using Flask. The received feedback is immediately saved to an SQLite database.

[0247] The server sends a "Received" message to the worker's terminal to confirm that the feedback was sent correctly.

[0248] 3. Classification of Opinions

[0249] The server uses Spacy to analyze the received comments using natural language processing and classify them into specific topics or categories. For example, the comment "The project is progressing slowly" would be classified under the category "Project Management."

[0250] The classification results are stored in the database.

[0251] 4. Agenda Generation

[0252] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the key issues and corresponding proposals are organized for each category.

[0253] The generated agenda is saved in the database.

[0254] 5. Agenda notification

[0255] The server sends the generated agenda to the terminals of workers and administrators, notifying them. Upon notification, the terminals display information about the agenda via pop-up messages or email.

[0256] Specific example

[0257] The worker enters the following comments:

[0258] 1. "The project is progressing slowly."

[0259] 2. "There is a lack of communication."

[0260] 3. "We should introduce new tools."

[0261] 4. "The priorities of the tasks are unclear."

[0262] These opinions are sent to the server's API endpoint and stored in a database. The server uses Spacy to categorize these opinions as follows:

[0263] Category 1: "Project Management"

[0264] Issues: "Slow progress," "Unclear priorities"

[0265] Proposal: "Introduction of new tools"

[0266] Category 2: "Communication"

[0267] Problem: "Lack of communication"

[0268] Next, the server generates an agenda from the classified results and sends an agenda like the following to the terminals of workers and administrators:

[0269] Category 1: "Project Management"

[0270] assignment:

[0271] Slow progress

[0272] Priorities are unclear

[0273] suggestion:

[0274] Introducing new tools

[0275] Category 2: "Communication"

[0276] assignment:

[0277] There is a lack of communication.

[0278] Example of a prompt:

[0279] The opinion that the progress is slow should be classified into the category of project management.

[0280] The opinion that there is a lack of communication should be classified into the category of communication.

[0281] This enables the effective collection of opinions from workers, rapid classification and sorting, and notification to the administrator.

[0282] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0283] (Flow of program processing)

[0284] Processing step

[0285] Step 1: <00009所03>

[0286] Collection of opinions

[0287] Input: The user inputs an opinion into the interface of a smartphone or a factory robot.

[0288] Operation: The user inputs an opinion into the text box of the interface and presses the send button. The opinion is configured as an HTTP request.

[0289] Output: Opinion data sent to the server.

[0290] Specific example: A worker inputs and sends "The progress of the project is slow".

[0291] Step 2:

[0292] Receiving and saving opinions

[0293] Input: The server receives an HTTP request.

[0294] Action: The server uses Flask to receive opinions at the API endpoint and saves the request data to a SQLite database.

[0295] Output: The saved opinion data and a message indicating the reception is complete.

[0296] Specific example: The Flask API saves the opinion "The project progress is slow" to the database and sends a reception completion message to the user terminal.

[0297] Step 3:

[0298] Opinion classification

[0299] Input: The received opinion data.

[0300] Action: The server analyzes the opinion using Spacy and classifies it into specific topics or categories through natural language processing. This includes text tokenization and entity recognition.

[0301] Output: The classified opinion data.

[0302] Specific example: The opinion "The progress is slow" is classified into the "Project management" category.

[0303] Step 4:

[0304] Agenda generation

[0305] Input: The classified opinion data.

[0306] Action: The server extracts important issues and proposals based on the classification results and generates an agenda for each category. It organizes the text of the issues and proposals and formats them in an easy-to-read form.

[0307] Output: Agenda data.

[0308] Specific example: An agenda is generated in the "Project Management" category that combines the issue of "slow progress" and the suggestion of "introducing a new tool."

[0309] Step 5:

[0310] Agenda notification

[0311] Input: Generated agenda data.

[0312] Operation: The server sends the agenda to the user's device and sends a notification. Information about the agenda is displayed via a pop-up message or email.

[0313] Output: Agenda notification displayed on the user's terminal.

[0314] Specific example: The administrator's smartphone receives a pop-up message notification for an agenda item in the "Project Management" category.

[0315] (Example of a prompt message)

[0316] "Opinions that progress is slow should be categorized under project management."

[0317] "The opinion that there is a lack of communication should be categorized under communication."

[0318] 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.

[0319] One embodiment of the present invention combines a system that effectively collects, classifies, and organizes user input, and summarizes important issues and proposals as an agenda, with an emotion engine that recognizes the user's emotions. This system provides the following functions:

[0320] Gathering opinions

[0321] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[0322] Receiving and saving feedback

[0323] The server has an API endpoint that receives feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[0324] Classification of Opinions and Sentiment Analysis

[0325] 1. Classification of opinions:

[0326] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[0327] 2. Sentiment analysis:

[0328] The server then uses an emotion engine to analyze the emotions contained in the user's comments. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each comment.

[0329] The analysis results are saved back into the database. Based on this sentiment information, the accuracy of opinion classification improves, enabling classifications that reflect the user's intentions and emotions.

[0330] Agenda generation

[0331] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. Furthermore, a flexible agenda is created that takes sentiment information into account.

[0332] The generated agenda is saved in the database and formatted in a user-friendly format.

[0333] Agenda notification

[0334] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0335] Specific example

[0336] The user enters the following comments:

[0337] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[0338] 2. "Lack of communication" (Emotion: Confusion)

[0339] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[0340] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[0341] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[0342] Category 1: "Project Management"

[0343] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0344] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0345] Category 2: "Communication"

[0346] Problem: "Lack of communication" (Emotion: Confusion)

[0347] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[0348] Category 1: "Project Management"

[0349] assignment:

[0350] Progress is slow (emotion: dissatisfied)

[0351] Priorities are unclear (emotion: anxiety)

[0352] suggestion:

[0353] Introducing a new tool (emotion: suggestion)

[0354] Category 2: "Communication"

[0355] assignment:

[0356] Lack of communication (Emotion: Confusion)

[0357] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0358] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[0359] The following describes the processing flow.

[0360] Program processing flow

[0361] Step 1: Enter and submit your comments.

[0362] Terminal:

[0363] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[0364] Specific actions:

[0365] 1. The user enters their opinion in the text box.

[0366] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[0367] Step 2: Receiving and saving feedback

[0368] server:

[0369] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[0370] Specific actions:

[0371] 1. The server receives a request to submit feedback from the terminal.

[0372] 2. Save the received feedback to the database.

[0373] 3. After saving is complete, a "Received" message will be sent to the device.

[0374] Step 3: Classifying opinions and analyzing sentiment

[0375] server:

[0376] The server extracts opinions from the database and classifies them using natural language processing (NLP). It also uses an emotion engine to recognize the emotions contained in the opinions.

[0377] Specific actions:

[0378] 1. The server reads unclassified comments from the database.

[0379] 2. Pass the opinions to a natural language processing module and classify them by topic.

[0380] 3. Pass the categorized opinions to the sentiment engine to generate sentiment data.

[0381] 4. Re-save the emotion data and classification results to the database.

[0382] Step 4: Agenda Generation

[0383] server:

[0384] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. The agenda organizes the issues and proposals for each topic along with sentiment information.

[0385] Specific actions:

[0386] 1. The server retrieves pre-classified opinion and sentiment data from the database.

[0387] 2. Extract the main issues and proposals, as well as related sentiment information, for each category.

[0388] 3. Summarize the issues and proposals to create an agenda.

[0389] 4. Save the agenda to the database.

[0390] Step 5: Agenda Notification

[0391] server:

[0392] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0393] Specific actions:

[0394] 1. The server reads the generated agenda from the database.

[0395] 2. Prepare the agenda in a format suitable for notifying users.

[0396] 3. Send the agenda to the user's device and notify them.

[0397] Step 6: User Verification

[0398] User:

[0399] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[0400] Specific actions:

[0401] 1. The user receives a notification and checks the agenda.

[0402] 2. Based on the agenda, formulate necessary plans and measures.

[0403] Specific example

[0404] The user enters the following comments:

[0405] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[0406] 2. "Lack of communication" (Emotion: Confusion)

[0407] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[0408] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[0409] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[0410] Category 1: "Project Management"

[0411] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0412] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0413] Category 2: "Communication"

[0414] Problem: "Lack of communication" (Emotion: Confusion)

[0415] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[0416] Category 1: "Project Management"

[0417] assignment:

[0418] Progress is slow (emotion: dissatisfied)

[0419] Priorities are unclear (emotion: anxiety)

[0420] suggestion:

[0421] Introducing a new tool (emotion: suggestion)

[0422] Category 2: "Communication"

[0423] assignment:

[0424] Lack of communication (Emotion: Confusion)

[0425] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0426] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[0427] (Example 2)

[0428] 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".

[0429] Traditional opinion gathering systems simply collect user input without adequately understanding their intentions and emotions, making it difficult to generate effective agendas. Furthermore, the lack of emotion-based classification and analysis made it challenging to extract specific issues and proposals that reflected user intent. This resulted in insufficient processing of user feedback, hindering efficient meetings and the development of action plans.

[0430] 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.

[0431] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions using natural language processing, means for analyzing the emotions contained in the opinions, means for extracting issues and proposals from the classified opinions and emotion information and generating an agenda, and means for transmitting and notifying the user of the agenda at their terminal. This enables the generation of a flexible and effective agenda that reflects the user's intentions and emotions.

[0432] A "user" is a person who operates this system and inputs their opinions.

[0433] "Opinions" refer to text data that users input for collection.

[0434] "Means of collection" refers to the interface of a device equipped with a UI form or text box for users to input their opinions.

[0435] "Means of transmission" refers to the function of a terminal that has a communication function for transmitting user input to a central processing unit.

[0436] A "central processing unit" is an information processing system, such as a server or computer, that performs tasks like classifying opinions and analyzing sentiment.

[0437] "Natural language processing" is a technology that enables computers to understand, interpret, and process human language.

[0438] "Means of classification" refer to algorithms and generative AI models that use natural language processing to divide opinions into topics or categories.

[0439] "Means of analyzing emotions" refer to emotion engines and algorithms used to analyze the emotions of users contained in their opinions.

[0440] "Emotional information" refers to emotional data extracted from analyzed opinions.

[0441] A "problem" is an issue extracted from classified opinion and sentiment information.

[0442] A "proposal" is an idea or method extracted as a solution to a problem.

[0443] An "agenda" is a plan for a meeting or action plan generated based on categorized opinions and sentiment information.

[0444] "Means of notification" refers to communication methods such as email or pop-up messages used to convey the generated agenda to the user.

[0445] This invention employs a system that effectively collects, classifies, and organizes user input, summarizing important issues and proposals as an agenda, and combines this with an emotion engine that recognizes the user's emotions. The following hardware and software are used to implement this system.

[0446] Hardware to use:

[0447] 1. Device: A device used to input opinions (such as a personal computer or smartphone)

[0448] 2. Server: A computer system for data processing and storage, classification, and sentiment analysis.

[0449] Software to use:

[0450] 1. UI Form: An interface for users to input their opinions.

[0451] 2. API Endpoint: A means of communication for sending feedback from a device to a server.

[0452] 3. Database: A management system for storing opinions and analysis results (e.g., MySQL, PostgreSQL)

[0453] 4. Natural Language Processing Systems: NLP tools and algorithms for classifying opinions (e.g., BERT, generative AI models)

[0454] 5. Emotion Engine: A tool that analyzes the emotions contained in user feedback (e.g., IBM Watson® Natural Language Understanding, Microsoft® Text Analytics API)

[0455] 6. Notification System: Means for notifying users of the agenda (e.g., email, WebSockets, push notifications)

[0456] Program processing

[0457] Users utilize a UI form on their device to input their opinions during brainstorming sessions. When a user enters their opinion into a text box and presses the "Submit" button, the opinion is sent to the server's API endpoint via an HTTP POST request.

[0458] The server receives this opinion and immediately saves it to the database. Once saving is complete, a "Received" message is sent to the terminal. The server then extracts the saved opinion from the database and uses natural language processing (NLP) to classify the opinion into topics and categories.

[0459] Next, the server uses an emotion engine to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. Based on this emotion information, the accuracy of opinion classification is improved, enabling classification that reflects the user's intentions and emotions.

[0460] Next, the server extracts key issues and suggestions based on the categorized opinions and sentiment information, and generates an agenda. This agenda organizes the main issues and suggestions for each category and is formatted in a flexible format. The generated agenda is saved in the database and notified to the user. Email and pop-up messages are often used as notification methods.

[0461] Specific example

[0462] Suppose a user enters the following opinion into their device and presses the submit button:

[0463] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[0464] 2. "Lack of communication" (Emotion: Confusion)

[0465] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[0466] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[0467] The server receives these opinions and stores them in a database. It then uses a natural language processing system to classify the opinions as follows:

[0468] Category 1: "Project Management"

[0469] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0470] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0471] Category 2: "Communication"

[0472] Problem: "Lack of communication" (Emotion: Confusion)

[0473] Next, taking emotional information into consideration, the server generates an agenda based on key issues and suggestions and sends it to the user as follows:

[0474] Category 1: "Project Management"

[0475] assignment:

[0476] Progress is slow (emotion: dissatisfied)

[0477] Priorities are unclear (emotion: anxiety)

[0478] suggestion:

[0479] Introducing a new tool (emotion: suggestion)

[0480] Category 2: "Communication"

[0481] assignment:

[0482] Lack of communication (Emotion: Confusion)

[0483] Example of a prompt

[0484] "Please categorize the following opinions and perform a sentiment analysis: 'Project progress is slow,' 'Lack of communication,' 'New tools should be introduced,' 'Work priorities are unclear.'"

[0485] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0486] Step 1:

[0487] The user enters their opinion.

[0488] Users input their opinions during brainstorming sessions using a dedicated UI form on their device. Specifically, they enter their opinions in a text box and press the "Submit" button. For example, they might input the opinion, "The project is progressing slowly." Input: User's opinion (text). Output: User's opinion (text).

[0489] Step 2:

[0490] The device sends the feedback.

[0491] When the user presses the submit button, the device sends the feedback to the server's API endpoint in the form of an HTTP POST request. The submitted data includes the text of the feedback entered by the user. Input: User's feedback (text). Output: Feedback data sent to the server (HTTP request).

[0492] Step 3:

[0493] The server receives and saves the feedback.

[0494] The server receives an HTTP POST request from the terminal, extracts the opinion data, and saves it to the database. MySQL, PostgreSQL, or other databases can be used for storage, depending on data integrity. Once saving is complete, the server sends a "reception complete" message to the terminal. Input: Sent opinion data (HTTP request). Output: Saved opinion data (database), reception complete message (HTTP response).

[0495] Step 4:

[0496] The server categorizes the opinions.

[0497] The server extracts opinion data stored in the database and classifies the opinions using natural language processing (NLP). Specifically, it uses algorithms such as BERT and generative AI models to classify opinions into topics and categories. For example, the opinion "the project is progressing slowly" would be classified under "project management." Input: Opinion data extracted from the database. Output: Classification results (topics and categories).

[0498] Step 5:

[0499] The server analyzes emotions.

[0500] The server uses an emotion engine to analyze the emotions contained in the classified opinion data. For example, it may utilize IBM Watson Natural Language Understanding or the Microsoft Text Analytics API. The emotion engine extracts emotion data (e.g., dissatisfaction, joy, suggestion, etc.) from the opinion text and generates emotion information corresponding to each opinion. Input: Classified opinion data. Output: Emotion information (extracted emotion data).

[0501] Step 6:

[0502] The server generates the agenda.

[0503] Based on classification results and sentiment information, the server extracts key issues and proposals and creates an agenda. For example, it organizes the main issues and corresponding proposals for each category and generates a flexible agenda that reflects sentiment information. Input: Classification results, sentiment information. Output: Generated agenda (list of issues and proposals).

[0504] Step 7:

[0505] The server notifies the user of the agenda.

[0506] The generated agenda is notified to the user's device. Notification methods include email and pop-up messages. SMTP servers, WebSockets, and push notification services can be used. For example, the generated agenda can be sent via email, with the content provided as a URL link. Input: Generated agenda. Output: Notification message sent to the user (email, pop-up notification).

[0507] (Application Example 2)

[0508] 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".

[0509] In factory production lines, there is a lack of efficient means to collect worker feedback and use it to improve work efficiency. Furthermore, because the emotions contained in worker feedback are not considered, the accuracy and effectiveness of improvement suggestions may decrease. To address this challenge, a system is needed that effectively collects and analyzes user feedback and emotions, and generates and notifies appropriate agendas.

[0510] 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 opinions input by the user, means for transmitting the opinions to a central processing device, means for classifying the opinions in the central processing device using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting and notifying the user of the agenda to the user's terminal, means for generating emotion information using an emotion engine that recognizes the emotions contained in the user's opinions, and means for improving the accuracy of agenda generation based on the emotion information. This makes it possible to effectively collect and analyze the opinions and emotions of workers and create and notify them of an agenda that includes specific points for improvement.

[0511] A "user" is an entity that uses the system to input opinions and receives agenda generation and notifications.

[0512] "Opinions" refer to text information entered by users through the system, and include issues and suggestions.

[0513] A "central processing unit" is a device that functions as a server, receiving, classifying, and generating an agenda based on opinions submitted by users.

[0514] "Natural language processing" is a technique used by central processing units to classify opinions into topics and categories, and is a means of analyzing text data.

[0515] An "emotion engine" is a technology or tool that recognizes the emotions contained in a user's opinion and generates emotional information.

[0516] An "agenda" is a compilation of issues and proposals extracted from opinions, presented in a format that is easy for users to understand.

[0517] A "database" is an information storage system for saving received opinions and generated sentiment information and agendas.

[0518] "Notification" refers to the act of informing a user's device of a generated agenda, and includes methods such as email and pop-up messages.

[0519] "Emotional information" refers to emotional data contained in an opinion, generated by an emotion engine.

[0520] One embodiment of the present invention relates to a system for collecting and analyzing the opinions and feelings of workers on a factory production line to generate an efficient agenda. This system is implemented with the following configuration and procedure.

[0521] System Configuration

[0522] 1. Gathering opinions

[0523] Users (workers) input their opinions using smart glasses or tablets. The input opinions are sent to the central processing unit (server) via a dedicated UI form.

[0524] 2. Receiving and saving feedback

[0525] The server receives the feedback sent from the device via the API endpoint and stores it in the database. The server verifies that the feedback has been saved correctly and sends a "Received" message to the device.

[0526] 3. Classification of Opinions and Sentiment Analysis

[0527] The server extracts opinions from the database and uses natural language processing (NLP) to categorize them into topics and categories. It uses a generative AI model to apply an algorithm that automatically categorizes the opinions.

[0528] Next, an emotion engine is used to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. This improves the accuracy of opinion classification and enables classification that reflects the user's intentions and emotions.

[0529] 4. Agenda Generation

[0530] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. This agenda is organized by category and created in a flexible and effective format.

[0531] 5. Agenda notification

[0532] The generated agenda is sent from the server to the user's device. The notification is sent via methods such as email or a pop-up message, allowing the user to review it and efficiently proceed with their next action plan.

[0533] Program processing

[0534] The server uses the following hardware and software:

[0535] Hardware: Factory robots, smart devices (smart glasses, tablets).

[0536] Software: Python, Node.js, MongoDB, React Native.

[0537] The server classifies opinions using natural language processing (NLP) and utilizes a generative AI model such as OpenAI's GPT-3. Similarly, the emotion engine extracts emotion data using an AI model and categorizes it appropriately.

[0538] Specific example

[0539] The user enters the following comments:

[0540] 1. "The project is progressing slowly."

[0541] 2. "There is a lack of communication."

[0542] 3. "We should introduce new tools."

[0543] 4. "The priorities of the tasks are unclear."

[0544] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[0545] Category 1: "Project Management"

[0546] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0547] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0548] Category 2: "Communication"

[0549] Problem: "Lack of communication" (Emotion: Confusion)

[0550] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[0551] Category 1: "Project Management"

[0552] assignment:

[0553] Progress is slow (emotion: dissatisfied)

[0554] Priorities are unclear (emotion: anxiety)

[0555] suggestion:

[0556] Introducing a new tool (emotion: suggestion)

[0557] Category 2: "Communication"

[0558] assignment:

[0559] Lack of communication (Emotion: Confusion)

[0560] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0561] Example of a prompt

[0562] The following are some examples of prompt statements that can be input to a generative AI model:

[0563] The user submitted the following feedback:

[0564] 1. The project is progressing slowly.

[0565] 2. Lack of communication

[0566] 3. New tools should be introduced.

[0567] 4. The priorities of the tasks are unclear.

[0568] Classify these opinions and feelings, and generate an agenda based on the main issues and suggestions. Feelings can be categorized as "dissatisfaction," "confusion," "suggestions," "anxiety," etc.

[0569] As described above, the system of the present invention is carried out in a series of steps, starting with opinion input, followed by opinion saving, classification, sentiment analysis, agenda generation, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] Gathering opinions

[0573] Users enter their opinions into a dedicated UI form using smart glasses or a tablet. The opinion input form includes a text box and a submit button. Users enter their opinions into the text box and confirm their opinions by pressing the submit button. At this time, the input is text information, and the output is the text data of the entered opinion.

[0574] Step 2:

[0575] Submit your feedback

[0576] The terminal sends the input opinion text data to the server. The transmission is done using an HTTP POST request to the API endpoint. The input is the opinion text data obtained in step 1, and the output is the opinion data sent to the server.

[0577] Step 3:

[0578] Receiving and saving feedback

[0579] The server receives opinion data sent from the terminal. The server stores the received opinion data in a database. At this time, the input is the sent opinion data, and the output is the opinion data stored in the database. Once saving is complete, the server sends a "reception complete" message to the terminal.

[0580] Step 4:

[0581] Classification of opinions

[0582] The server extracts opinion data from the database and uses natural language processing (NLP) to classify the opinions into topics and categories. A generative AI model is used to automatically classify the opinions based on their content. In this process, the input is the opinion data extracted from the database, and the output is the classified result data.

[0583] Step 5:

[0584] sentiment analysis

[0585] The server performs sentiment analysis on the classified opinion data using an emotion engine. The emotion engine uses an AI model to extract sentiment data from the text and associate sentiment information with each opinion. In this process, the input is the classified opinion data, and the output is the opinion data with sentiment information added.

[0586] Step 6:

[0587] Agenda generation

[0588] The server extracts key issues and proposals based on categorized opinion data and sentiment information, and generates an agenda. In this process, the input is opinion data with added sentiment information, and the output is the generated agenda data. The agenda is organized by category and created in a flexible and effective format.

[0589] Step 7:

[0590] Agenda notification

[0591] The generated agenda is notified from the server to the user's terminal. This notification is sent via email, pop-up message, or other means. In this case, the input is the generated agenda data, and the output is the agenda notification received by the user. The user can then review this and efficiently proceed with their next action plan.

[0592] 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.

[0593] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] 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.

[0595] [Second Embodiment]

[0596] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system provides the following functions:

[0609] Gathering opinions

[0610] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[0611] Receiving and saving feedback

[0612] The server has an API endpoint for receiving feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[0613] Classification of opinions

[0614] The server classifies the received opinions using natural language processing (NLP). This processing is performed using generative AI or other appropriate algorithms. The purpose of classification is to divide the opinions into topics or categories. For example, the opinion "the project is progressing slowly" would be classified under the category "project management." The classification results are stored in a database.

[0615] Agenda generation

[0616] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. The generated agenda is stored in a database and formatted in a user-friendly format.

[0617] Agenda notification

[0618] The server sends the generated agenda to the user's device and notifies them. By referring to the agenda, the user can effectively utilize the results of the brainstorming session. When the notification is sent, information about the agenda will be displayed on the user's device via a pop-up message or email.

[0619] Specific example

[0620] For example, a user enters the following comment:

[0621] 1. "The project is progressing slowly."

[0622] 2. "There is a lack of communication."

[0623] 3. "We should introduce new tools."

[0624] 4. "The priorities of the tasks are unclear."

[0625] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[0626] Category 1: "Project Management"

[0627] Issues: "Slow progress," "Unclear priorities"

[0628] Proposal: "Introduction of new tools"

[0629] Category 2: "Communication"

[0630] Problem: "Lack of communication"

[0631] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[0632] Category 1: "Project Management"

[0633] assignment:

[0634] Slow progress

[0635] Priorities are unclear

[0636] suggestion:

[0637] Introducing new tools

[0638] Category 2: "Communication"

[0639] assignment:

[0640] There is a lack of communication.

[0641] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0642] As described above, the present invention is a system that can fairly and efficiently classify opinions from brainstorming sessions and compile important issues and proposals into an agenda.

[0643] The following describes the processing flow.

[0644] Program processing flow

[0645] Step 1: Enter and submit your comments.

[0646] Terminal:

[0647] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[0648] Specific actions:

[0649] 1. The user enters their opinion in the text box.

[0650] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[0651] Step 2: Receiving and saving feedback

[0652] server:

[0653] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[0654] Specific actions:

[0655] 1. The server receives a request to submit feedback from the terminal.

[0656] 2. Save the received feedback to the database.

[0657] 3. After saving is complete, a "Received" message will be sent to the device.

[0658] Step 3: Classification of Opinions

[0659] server:

[0660] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[0661] Specific actions:

[0662] 1. The server reads unclassified comments from the database.

[0663] 2. Pass the opinions to the NLP module and obtain the classification results.

[0664] 3. Re-save the categorized opinions in the database.

[0665] Step 4: Agenda Generation

[0666] server:

[0667] The server extracts key issues and proposals from the categorized opinions and generates an agenda. The agenda organizes the issues and proposals for each topic.

[0668] Specific actions:

[0669] 1. The server retrieves pre-classified opinions from the database.

[0670] 2. Identify the main issues and proposals for each category.

[0671] 3. Summarize the issues and proposals to create an agenda.

[0672] 4. Save the agenda to the database.

[0673] Step 5: Agenda Notification

[0674] server:

[0675] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0676] Specific actions:

[0677] 1. The server reads the generated agenda from the database.

[0678] 2. Prepare the agenda in a format suitable for notifying users.

[0679] 3. Send the agenda to the user's device and notify them.

[0680] Step 6: User Verification

[0681] User:

[0682] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[0683] Specific actions:

[0684] 1. The user receives a notification and checks the agenda.

[0685] 2. Based on the agenda, formulate necessary plans and measures.

[0686] Thus, the system of the present invention is carried out in a series of steps, starting with user input of opinions, followed by saving, classifying, generating an agenda, notification, and user confirmation.

[0687] (Example 1)

[0688] 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".

[0689] In traditional brainstorming sessions, it was difficult to efficiently collect, classify, and organize the opinions entered by users. Furthermore, there was no method for quickly and accurately generating an agenda summarizing key issues and proposals. This resulted in problems such as insufficient organization of opinions and the inability to create effective agendas.

[0690] 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.

[0691] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, and means for transmitting and notifying the user of the agenda to the user's terminal. This makes it possible to effectively collect, classify, and organize opinions submitted by users in brainstorming sessions, and to quickly compile important issues and proposals into an agenda.

[0692] A "user" is someone who uses the system to input opinions and participate in brainstorming sessions.

[0693] "Opinions" refer to suggestions, issues, and information and feedback that users input during a brainstorming session.

[0694] "Means of collection" refers to mechanisms or devices for collecting and acquiring opinions entered by users.

[0695] "Transmission means" refers to methods or devices for sending collected opinions to a central processing unit.

[0696] A "central processing unit" is a computer system or server that receives opinions sent by users and processes them accordingly.

[0697] "Natural language processing" is a field of computer science that aims to mechanically analyze opinions and understand their meaning and intent.

[0698] A "classification method" refers to a method or device that uses natural language processing to organize received opinions into topics or categories.

[0699] "Extraction methods" refer to methods or devices for identifying and extracting important issues and proposals from classified opinions.

[0700] An "agenda generation method" refers to a method or device for organizing extracted issues and proposals and compiling them into an agenda.

[0701] "Notification means" refers to a method or device for sending the generated agenda to the user's terminal to inform them.

[0702] A "device" refers to a computer or mobile device that a user uses to input opinions and receive notifications.

[0703] A "database" is a storage location or system for saving and managing information such as received opinions, classification results, and agendas.

[0704] "Means of formatting" refers to methods or devices for arranging an agenda into a format that is easy for users to see and understand.

[0705] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system is implemented using the following hardware and software.

[0706] Users enter their opinions using a dedicated UI form on their device. For example, a web form built with HTML and CSS could be used, with JavaScript triggering a click event for the submit button. This form would include text boxes and a submit button.

[0707] The terminal sends the inputted opinion to the server. The server receives the request via an API endpoint (e.g., a REST API). The server uses a Python framework (such as Flask or Django) to set up the API endpoint, and upon receiving the opinion, it saves the opinion to a database (such as MySQL or PostgreSQL) using a Python ORM (Object-Relational Mapping). If the saving of the opinion is successful, the server returns a "Received" message to the terminal.

[0708] The server classifies the stored opinions using natural language processing (NLP). This analysis uses a generative AI model (e.g., GPT-3) or other appropriate algorithm. For example, a Python script is used to retrieve opinion data from the database and generate prompt sentences to input into the AI ​​model. The AI ​​model returns the classification results, which are then stored in the database.

[0709] Next, the server extracts key issues and suggestions from the categorized opinions and generates an agenda. In this process, the main issues and suggestions are organized within each category. The generated agenda is then formatted in a user-friendly format, for example, using a formatting library such as Markdown.

[0710] The server sends the generated agenda to the user's device and notifies them. Notifications are sent via pop-up messages or email. For example, notifications can be sent to the device using a REST API or WebSocket, and email notifications use the SMTP protocol. Users refer to the notified agenda and effectively utilize the results of the brainstorming session.

[0711] Specific example

[0712] For example, suppose a user enters feedback such as "Project progress is slow," "There is a lack of communication," "A new tool should be introduced," or "The work priorities are unclear" into a terminal and presses the send button. The terminal sends these feedback to a server, which stores them in a database. The server then uses natural language processing to classify the stored feedback as follows:

[0713] Category 1: "Project Management"

[0714] Issues: "Slow progress," "Unclear priorities"

[0715] Proposal: "Introduction of new tools"

[0716] Category 2: "Communication"

[0717] Problem: "Lack of communication"

[0718] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[0719] Category 1: "Project Management"

[0720] assignment:

[0721] Slow progress

[0722] Priorities are unclear

[0723] suggestion:

[0724] Introducing new tools

[0725] Category 2: "Communication"

[0726] assignment:

[0727] There is a lack of communication.

[0728] Examples of prompt statements

[0729] The following are specific examples of prompt statements to be input into a generative AI model:

[0730] The following are the opinions submitted by users during the brainstorming session.

[0731] 1. The project is progressing slowly.

[0732] 2. Lack of communication

[0733] 3. New tools should be introduced.

[0734] 4. The priorities of the tasks are unclear.

[0735] Please categorize these opinions and list the issues and suggestions for each category.

[0736] The present invention is a system that can effectively collect, classify, and organize the opinions submitted by users as described above, and quickly compile important issues and proposals into an agenda.

[0737] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0738] Step 1:

[0739] The user enters their opinion. The user uses a dedicated UI form on their device (built with HTML and CSS) to enter their opinion. For example, they might enter "Project progress is slow" and click the "Submit" button. The input data is in text format.

[0740] Specific actions:

[0741] The device uses JavaScript to detect the click event of the submit button and prepares the feedback as submission data.

[0742] Step 2:

[0743] The terminal sends the inputted opinion to the server. The terminal sends the opinion data as an HTTP request to the server's API endpoint.

[0744] Input: User-submitted opinions

[0745] Output: Opinion data sent to the server

[0746] Specific actions:

[0747] The device sends feedback to the server using JavaScript's Fetch API or XMLHttpRequest.

[0748] Step 3:

[0749] The server receives the feedback and saves it to a database. The server receives feedback data via an API endpoint (for example, using the Flask or Django framework in Python) and saves it to a database (MySQL or PostgreSQL). If the save is successful, the server returns a "received" message to the terminal.

[0750] Input: Opinion data sent from the device

[0751] Output: Opinion data stored in the database, "Received" message

[0752] Specific actions:

[0753] The server receives opinion data using an API built with Flask or Django and stores it in a database using an ORM.

[0754] Step 4:

[0755] The server analyzes and classifies the stored opinions using natural language processing (NLP). The server generates prompt sentences to input the stored opinion data into a generating AI model (e.g., GPT-3). The model classifies the opinions into categories (e.g., "project management" or "communication") and returns the results. The classification results are stored in a database.

[0756] Input: Opinion data stored in the database

[0757] Output: Classified opinion data, classification results

[0758] Specific actions:

[0759] The server uses a Python script to acquire opinion data and create prompt statements to input into the generative AI model.

[0760] The classification results obtained from the AI ​​model are stored in a database.

[0761] Step 5:

[0762] The server extracts key issues and suggestions from categorized opinions and generates an agenda. It organizes the main issues and suggestions by category and creates the agenda using a formatting library such as Markdown. This agenda is then stored in a database.

[0763] Input: Classified opinion data

[0764] Output: Generated agenda

[0765] Specific actions:

[0766] The server uses a Python script to process the classification results and extract issues and suggestions.

[0767] Format the agenda using a formatting library and save it to the database.

[0768] Step 6:

[0769] The server sends the generated agenda to the user's device and notifies them. This notification is delivered via a pop-up message or email. This allows the user to view the agenda.

[0770] Input: Generated agenda

[0771] Output: Agenda notification displayed on the user's device.

[0772] Specific actions:

[0773] The server sends the agenda to the terminal using a REST API or WebSocket.

[0774] For email notifications, emails are sent using SMTP.

[0775] The above processing steps enable the effective collection, classification, and organization of user input, allowing for the rapid compilation of important issues and suggestions into an agenda.

[0776] (Application Example 1)

[0777] 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."

[0778] In industrial environments, there is a need for methods to effectively collect worker opinions and improvement suggestions, appropriately classify and organize them, and summarize important issues and proposals. Furthermore, an efficient system is required to quickly notify managers of the collected opinions and implement them as improvement plans.

[0779] 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.

[0780] In this invention, the server includes means for collecting user inputs, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting the agenda to the user's terminal and notifying them, means for industrial workers to input their opinions into an interface, and means for collecting opinions via factory robots and transmitting them to the server. This makes it possible to effectively collect opinions from workers, quickly classify and organize them, and notify administrators.

[0781] (Definition of terms)

[0782] A "user" is a person, such as a worker or manager, who uses this system to input opinions or suggestions.

[0783] "Feedback" refers to improvement suggestions, reports, and comments on issues entered by users.

[0784] "Means of collection" refer to devices such as interfaces and sensors that electronically capture opinions entered by users.

[0785] "Means of transmission" refers to the process or device for sending collected opinions to a central processing unit via a communication network.

[0786] A "central processing unit" is a computer system or server used to process incoming opinions.

[0787] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[0788] "Classification methods" refer to processes or devices that divide opinions collected using natural language processing into specific categories or topics.

[0789] A "problem" is a problem raised by a user that requires a solution.

[0790] A "proposal" is a solution or improvement suggested by a user to address a problem.

[0791] An "agenda" is a list that organizes the issues and proposals extracted from categorized opinions.

[0792] "Means of notification" refers to processes or devices for informing users' devices of the generated agenda.

[0793] "Industrial environment workers" are employees who perform work in industrial facilities such as factories.

[0794] An "interface" refers to a device or software used by workers to input their opinions.

[0795] A "factory robot" is an automated device that performs tasks automatically within a factory and collects feedback.

[0796] A "database" is an information storage system used to store and manage received opinions and generated agendas.

[0797] overview

[0798] This invention is a system that collects opinions and suggestions from workers in an industrial environment, classifies them using natural language processing (NLP), extracts and organizes important issues and helpful suggestions, and generates an agenda. The generated agenda is notified to the terminals of workers and managers and used for rapid decision-making and improvement activities.

[0799] Hardware and software to be used

[0800] 1. Hardware

[0801] Smartphone: An interface for workers to input their opinions.

[0802] Factory robots: These robots have the function of collecting opinions from the industrial environment and sending them to a server.

[0803] Server: A central processing unit that receives, classifies, and generates agendas for opinions.

[0804] 2. Software

[0805] Python: A programming language used to write the main parts of a program.

[0806] Flask: A web framework for building API servers.

[0807] Spacy: A library for performing natural language processing (NLP).

[0808] SQLite: A database management system.

[0809] Processing flow

[0810] 1. Gathering opinions

[0811] Workers input their opinions using a smartphone or an interface mounted on a factory robot. The interface includes text boxes and a submit button.

[0812] The worker enters their opinion and presses the submit button, which sends the opinion to the server.

[0813] 2. Receiving and saving feedback

[0814] The server receives feedback via an API endpoint built using Flask. The received feedback is immediately saved to an SQLite database.

[0815] The server sends a "Received" message to the worker's terminal to confirm that the feedback was sent correctly.

[0816] 3. Classification of Opinions

[0817] The server uses Spacy to analyze the received comments using natural language processing and classify them into specific topics or categories. For example, the comment "The project is progressing slowly" would be classified under the category "Project Management."

[0818] The classification results are stored in the database.

[0819] 4. Agenda Generation

[0820] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the key issues and corresponding proposals are organized for each category.

[0821] The generated agenda is saved in the database.

[0822] 5. Agenda notification

[0823] The server sends the generated agenda to the terminals of workers and administrators, notifying them. Upon notification, the terminals display information about the agenda via pop-up messages or email.

[0824] Specific example

[0825] The worker enters the following comments:

[0826] 1. "The project is progressing slowly."

[0827] 2. "There is a lack of communication."

[0828] 3. "We should introduce new tools."

[0829] 4. "The priorities of the tasks are unclear."

[0830] These opinions are sent to the server's API endpoint and stored in a database. The server uses Spacy to categorize these opinions as follows:

[0831] Category 1: "Project Management"

[0832] Issues: "Slow progress," "Unclear priorities"

[0833] Proposal: "Introduction of new tools"

[0834] Category 2: "Communication"

[0835] Problem: "Lack of communication"

[0836] Next, the server generates an agenda from the classified results and sends an agenda like the following to the terminals of workers and administrators:

[0837] Category 1: "Project Management"

[0838] assignment:

[0839] Slow progress

[0840] Priorities are unclear

[0841] suggestion:

[0842] Introducing new tools

[0843] Category 2: "Communication"

[0844] assignment:

[0845] There is a lack of communication.

[0846] Example of a prompt:

[0847] "The opinion that progress is slow should be categorized under project management."

[0848] "The opinion that there is a lack of communication should be categorized under communication."

[0849] This makes it possible to effectively collect feedback from workers, quickly classify and organize it, and notify managers.

[0850] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0851] (Program processing flow)

[0852] Processing steps

[0853] Step 1:

[0854] Gathering opinions

[0855] Input: Users input their opinions into a smartphone or factory robot interface.

[0856] Operation: The user enters their opinion into a text box on the interface and presses the submit button. The opinion is structured as an HTTP request.

[0857] Output: Opinion data sent to the server.

[0858] Specific example: A worker types and sends the message, "The project is progressing slowly."

[0859] Step 2:

[0860] Receiving and saving feedback

[0861] Input: The server receives an HTTP request.

[0862] Operation: The server uses Flask to receive feedback at the API endpoint and saves the request data to an SQLite database.

[0863] Output: Saved opinion data and a message confirming receipt.

[0864] Specific example: The Flask API saves the feedback "Project progress is slow" to a database and sends a message to the user's terminal confirming receipt.

[0865] Step 3:

[0866] Classification of opinions

[0867] Input: Received opinion data.

[0868] Operation: The server uses Spacy to analyze opinions and classify them into specific topics or categories using natural language processing. This includes text tokenization and entity recognition.

[0869] Output: Classified opinion data.

[0870] Specific example: The opinion that "progress is slow" is categorized under "project management."

[0871] Step 4:

[0872] Agenda generation

[0873] Input: Classified opinion data.

[0874] Operation: The server extracts key issues and proposals based on the classification results and generates an agenda for each category. It organizes the text of the issues and proposals and formats them into an easy-to-read format.

[0875] Output: Agenda data.

[0876] Specific example: An agenda is generated in the "Project Management" category that combines the issue of "slow progress" and the suggestion of "introducing a new tool."

[0877] Step 5:

[0878] Agenda notification

[0879] Input: Generated agenda data.

[0880] Operation: The server sends the agenda to the user's device and sends a notification. Information about the agenda is displayed via a pop-up message or email.

[0881] Output: Agenda notification displayed on the user's terminal.

[0882] Specific example: The administrator's smartphone receives a pop-up message notification for an agenda item in the "Project Management" category.

[0883] (Example of a prompt message)

[0884] "The opinion that progress is slow should be categorized under project management."

[0885] "The opinion that there is a lack of communication should be categorized under communication."

[0886] 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.

[0887] One embodiment of the present invention combines a system that effectively collects, classifies, and organizes user input, and summarizes important issues and proposals as an agenda, with an emotion engine that recognizes the user's emotions. This system provides the following functions:

[0888] Gathering opinions

[0889] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[0890] Receiving and saving feedback

[0891] The server has an API endpoint that receives feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[0892] Classification of Opinions and Sentiment Analysis

[0893] 1. Classification of opinions:

[0894] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[0895] 2. Sentiment analysis:

[0896] The server then uses an emotion engine to analyze the emotions contained in the user's comments. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each comment.

[0897] The analysis results are saved back into the database. Based on this sentiment information, the accuracy of opinion classification improves, enabling classifications that reflect the user's intentions and emotions.

[0898] Agenda generation

[0899] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. Furthermore, a flexible agenda is created that takes sentiment information into account.

[0900] The generated agenda is saved in the database and formatted in a user-friendly format.

[0901] Agenda notification

[0902] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0903] Specific example

[0904] The user enters the following comments:

[0905] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[0906] 2. "Lack of communication" (Emotion: Confusion)

[0907] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[0908] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[0909] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[0910] Category 1: "Project Management"

[0911] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0912] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0913] Category 2: "Communication"

[0914] Problem: "Lack of communication" (Emotion: Confusion)

[0915] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[0916] Category 1: "Project Management"

[0917] assignment:

[0918] Progress is slow (emotion: dissatisfied)

[0919] Priorities are unclear (emotion: anxiety)

[0920] suggestion:

[0921] Introducing a new tool (emotion: suggestion)

[0922] Category 2: "Communication"

[0923] assignment:

[0924] Lack of communication (Emotion: Confusion)

[0925] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0926] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[0927] The following describes the processing flow.

[0928] Program processing flow

[0929] Step 1: Enter and submit your comments.

[0930] Terminal:

[0931] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[0932] Specific actions:

[0933] 1. The user enters their opinion in the text box.

[0934] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[0935] Step 2: Receiving and saving feedback

[0936] server:

[0937] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[0938] Specific actions:

[0939] 1. The server receives a request to submit feedback from the terminal.

[0940] 2. Save the received feedback to the database.

[0941] 3. After saving is complete, a "Received" message will be sent to the device.

[0942] Step 3: Classifying opinions and analyzing sentiment

[0943] server:

[0944] The server extracts opinions from the database and classifies them using natural language processing (NLP). It also uses an emotion engine to recognize the emotions contained in the opinions.

[0945] Specific actions:

[0946] 1. The server reads unclassified comments from the database.

[0947] 2. Pass the opinions to a natural language processing module and classify them by topic.

[0948] 3. Pass the categorized opinions to the sentiment engine to generate sentiment data.

[0949] 4. Re-save the emotion data and classification results to the database.

[0950] Step 4: Agenda Generation

[0951] server:

[0952] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. The agenda organizes the issues and proposals for each topic along with sentiment information.

[0953] Specific actions:

[0954] 1. The server retrieves pre-classified opinion and sentiment data from the database.

[0955] 2. Extract the main issues and proposals, as well as related sentiment information, for each category.

[0956] 3. Summarize the issues and proposals to create an agenda.

[0957] 4. Save the agenda to the database.

[0958] Step 5: Agenda Notification

[0959] server:

[0960] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[0961] Specific actions:

[0962] 1. The server reads the generated agenda from the database.

[0963] 2. Prepare the agenda in a format suitable for notifying users.

[0964] 3. Send the agenda to the user's device and notify them.

[0965] Step 6: User Verification

[0966] User:

[0967] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[0968] Specific actions:

[0969] 1. The user receives a notification and checks the agenda.

[0970] 2. Based on the agenda, formulate necessary plans and measures.

[0971] Specific example

[0972] The user enters the following comments:

[0973] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[0974] 2. "Lack of communication" (Emotion: Confusion)

[0975] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[0976] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[0977] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[0978] Category 1: "Project Management"

[0979] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[0980] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[0981] Category 2: "Communication"

[0982] Problem: "Lack of communication" (Emotion: Confusion)

[0983] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[0984] Category 1: "Project Management"

[0985] assignment:

[0986] Progress is slow (emotion: dissatisfied)

[0987] Priorities are unclear (emotion: anxiety)

[0988] suggestion:

[0989] Introducing a new tool (emotion: suggestion)

[0990] Category 2: "Communication"

[0991] assignment:

[0992] Lack of communication (Emotion: Confusion)

[0993] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[0994] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[0995] (Example 2)

[0996] 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".

[0997] Traditional opinion gathering systems simply collect user input without adequately understanding their intentions and emotions, making it difficult to generate effective agendas. Furthermore, the lack of emotion-based classification and analysis made it challenging to extract specific issues and proposals that reflected user intent. This resulted in insufficient processing of user feedback, hindering efficient meetings and the development of action plans.

[0998] 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.

[0999] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions using natural language processing, means for analyzing the emotions contained in the opinions, means for extracting issues and proposals from the classified opinions and emotion information and generating an agenda, and means for transmitting and notifying the user of the agenda at their terminal. This enables the generation of a flexible and effective agenda that reflects the user's intentions and emotions.

[1000] A "user" is a person who operates this system and inputs their opinions.

[1001] "Opinions" refer to text data that users input for collection.

[1002] "Means of collection" refers to the interface of a device equipped with a UI form or text box for users to input their opinions.

[1003] "Means of transmission" refers to the function of a terminal that has a communication function for transmitting user input to a central processing unit.

[1004] A "central processing unit" is an information processing system, such as a server or computer, that performs tasks like classifying opinions and analyzing sentiment.

[1005] "Natural language processing" is a technology that enables computers to understand, interpret, and process human language.

[1006] "Means of classification" refer to algorithms and generative AI models that use natural language processing to divide opinions into topics or categories.

[1007] "Means of analyzing emotions" refer to emotion engines and algorithms used to analyze the emotions of users contained in their opinions.

[1008] "Emotional information" refers to emotional data extracted from analyzed opinions.

[1009] A "problem" is an issue extracted from classified opinion and sentiment information.

[1010] A "proposal" is an idea or method extracted as a solution to a problem.

[1011] An "agenda" is a plan for a meeting or action plan generated based on categorized opinions and sentiment information.

[1012] "Means of notification" refers to communication methods such as email or pop-up messages used to convey the generated agenda to the user.

[1013] This invention employs a system that effectively collects, classifies, and organizes user input, summarizing important issues and proposals as an agenda, and combines this with an emotion engine that recognizes the user's emotions. The following hardware and software are used to implement this system.

[1014] Hardware to use:

[1015] 1. Device: A device used to input opinions (such as a personal computer or smartphone)

[1016] 2. Server: A computer system for data processing and storage, classification, and sentiment analysis.

[1017] Software to use:

[1018] 1. UI Form: An interface for users to input their opinions.

[1019] 2. API Endpoint: A means of communication for sending feedback from a device to a server.

[1020] 3. Database: A management system for storing opinions and analysis results (e.g., MySQL, PostgreSQL)

[1021] 4. Natural Language Processing Systems: NLP tools and algorithms for classifying opinions (e.g., BERT, generative AI models)

[1022] 5. Emotion Engine: A tool that analyzes the emotions contained in user feedback (e.g., IBM Watson Natural Language Understanding, Microsoft Text Analytics API)

[1023] 6. Notification System: Means for notifying users of the agenda (e.g., email, WebSockets, push notifications)

[1024] Program processing

[1025] Users utilize a UI form on their device to input their opinions during brainstorming sessions. When a user enters their opinion into a text box and presses the "Submit" button, the opinion is sent to the server's API endpoint via an HTTP POST request.

[1026] The server receives this opinion and immediately saves it to the database. Once saving is complete, a "Received" message is sent to the terminal. The server then extracts the saved opinion from the database and uses natural language processing (NLP) to classify the opinion into topics and categories.

[1027] Next, the server uses an emotion engine to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. Based on this emotion information, the accuracy of opinion classification is improved, enabling classification that reflects the user's intentions and emotions.

[1028] Next, the server extracts key issues and suggestions based on the categorized opinions and sentiment information, and generates an agenda. This agenda organizes the main issues and suggestions for each category and is formatted in a flexible format. The generated agenda is saved in the database and notified to the user. Email and pop-up messages are often used as notification methods.

[1029] Specific example

[1030] Suppose a user enters the following opinion into their device and presses the submit button:

[1031] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[1032] 2. "Lack of communication" (Emotion: Confusion)

[1033] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[1034] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[1035] The server receives these opinions and stores them in a database. It then uses a natural language processing system to classify the opinions as follows:

[1036] Category 1: "Project Management"

[1037] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1038] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1039] Category 2: "Communication"

[1040] Problem: "Lack of communication" (Emotion: Confusion)

[1041] Next, taking emotional information into consideration, the server generates an agenda based on key issues and suggestions and sends it to the user as follows:

[1042] Category 1: "Project Management"

[1043] assignment:

[1044] Progress is slow (emotion: dissatisfied)

[1045] Priorities are unclear (emotion: anxiety)

[1046] suggestion:

[1047] Introducing a new tool (emotion: suggestion)

[1048] Category 2: "Communication"

[1049] assignment:

[1050] Lack of communication (Emotion: Confusion)

[1051] Example of a prompt

[1052] "Please categorize the following opinions and perform a sentiment analysis: 'Project progress is slow,' 'Lack of communication,' 'New tools should be introduced,' 'Work priorities are unclear.'"

[1053] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1054] Step 1:

[1055] The user enters their opinion.

[1056] Users input their opinions during brainstorming sessions using a dedicated UI form on their device. Specifically, they enter their opinions in a text box and press the "Submit" button. For example, they might input the opinion, "The project is progressing slowly." Input: User's opinion (text). Output: User's opinion (text).

[1057] Step 2:

[1058] The device sends the feedback.

[1059] When the user presses the submit button, the device sends the feedback to the server's API endpoint in the form of an HTTP POST request. The submitted data includes the text of the feedback entered by the user. Input: User's feedback (text). Output: Feedback data sent to the server (HTTP request).

[1060] Step 3:

[1061] The server receives and saves the feedback.

[1062] The server receives an HTTP POST request from the terminal, extracts the opinion data, and saves it to the database. MySQL, PostgreSQL, or other databases can be used for storage, depending on data integrity. Once saving is complete, the server sends a "reception complete" message to the terminal. Input: Sent opinion data (HTTP request). Output: Saved opinion data (database), reception complete message (HTTP response).

[1063] Step 4:

[1064] The server categorizes the opinions.

[1065] The server extracts opinion data stored in the database and classifies the opinions using natural language processing (NLP). Specifically, it uses algorithms such as BERT and generative AI models to classify opinions into topics and categories. For example, the opinion "the project is progressing slowly" would be classified under "project management." Input: Opinion data extracted from the database. Output: Classification results (topics and categories).

[1066] Step 5:

[1067] The server analyzes emotions.

[1068] The server uses an emotion engine to analyze the emotions contained in the classified opinion data. For example, it may utilize IBM Watson Natural Language Understanding or the Microsoft Text Analytics API. The emotion engine extracts emotion data (e.g., dissatisfaction, joy, suggestion, etc.) from the opinion text and generates emotion information corresponding to each opinion. Input: Classified opinion data. Output: Emotion information (extracted emotion data).

[1069] Step 6:

[1070] The server generates the agenda.

[1071] Based on classification results and sentiment information, the server extracts key issues and proposals and creates an agenda. For example, it organizes the main issues and corresponding proposals for each category and generates a flexible agenda that reflects sentiment information. Input: Classification results, sentiment information. Output: Generated agenda (list of issues and proposals).

[1072] Step 7:

[1073] The server notifies the user of the agenda.

[1074] The generated agenda is notified to the user's device. Notification methods include email and pop-up messages. SMTP servers, WebSockets, and push notification services can be used. For example, the generated agenda can be sent via email, with the content provided as a URL link. Input: Generated agenda. Output: Notification message sent to the user (email, pop-up notification).

[1075] (Application Example 2)

[1076] 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."

[1077] In factory production lines, there is a lack of efficient means to collect worker feedback and use it to improve work efficiency. Furthermore, because the emotions contained in worker feedback are not considered, the accuracy and effectiveness of improvement suggestions may decrease. To address this challenge, a system is needed that effectively collects and analyzes user feedback and emotions, and generates and notifies appropriate agendas.

[1078] 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 opinions input by the user, means for transmitting the opinions to a central processing device, means for classifying the opinions in the central processing device using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting and notifying the user of the agenda to the user's terminal, means for generating emotion information using an emotion engine that recognizes the emotions contained in the user's opinions, and means for improving the accuracy of agenda generation based on the emotion information. This makes it possible to effectively collect and analyze the opinions and emotions of workers and create and notify them of an agenda that includes specific points for improvement.

[1079] A "user" is an entity that uses the system to input opinions and receives agenda generation and notifications.

[1080] "Opinions" refer to text information entered by users through the system, and include issues and suggestions.

[1081] A "central processing unit" is a device that functions as a server, receiving, classifying, and generating an agenda based on opinions submitted by users.

[1082] "Natural language processing" is a technique used by central processing units to classify opinions into topics and categories, and is a means of analyzing text data.

[1083] An "emotion engine" is a technology or tool that recognizes the emotions contained in a user's opinion and generates emotional information.

[1084] An "agenda" is a compilation of issues and proposals extracted from opinions, presented in a format that is easy for users to understand.

[1085] A "database" is an information storage system for saving received opinions and generated sentiment information and agendas.

[1086] "Notification" refers to the act of informing a user's device of a generated agenda, and includes methods such as email and pop-up messages.

[1087] "Emotional information" refers to emotional data contained in an opinion, generated by an emotion engine.

[1088] One embodiment of the present invention relates to a system for collecting and analyzing the opinions and feelings of workers on a factory production line to generate an efficient agenda. This system is implemented with the following configuration and procedure.

[1089] System Configuration

[1090] 1. Gathering opinions

[1091] Users (workers) input their opinions using smart glasses or tablets. The input opinions are sent to the central processing unit (server) via a dedicated UI form.

[1092] 2. Receiving and saving feedback

[1093] The server receives the feedback sent from the device via the API endpoint and stores it in the database. The server verifies that the feedback has been saved correctly and sends a "Received" message to the device.

[1094] 3. Classification of Opinions and Sentiment Analysis

[1095] The server extracts opinions from the database and uses natural language processing (NLP) to categorize them into topics and categories. It uses a generative AI model to apply an algorithm that automatically categorizes the opinions.

[1096] Next, an emotion engine is used to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. This improves the accuracy of opinion classification and enables classification that reflects the user's intentions and emotions.

[1097] 4. Agenda Generation

[1098] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. This agenda is organized by category and created in a flexible and effective format.

[1099] 5. Agenda notification

[1100] The generated agenda is sent from the server to the user's device. The notification is sent via methods such as email or a pop-up message, allowing the user to review it and efficiently proceed with their next action plan.

[1101] Program processing

[1102] The server uses the following hardware and software:

[1103] Hardware: Factory robots, smart devices (smart glasses, tablets).

[1104] Software: Python, Node.js, MongoDB, React Native.

[1105] The server classifies opinions using natural language processing (NLP) and utilizes a generative AI model such as OpenAI's GPT-3. Similarly, the emotion engine extracts emotion data using an AI model and categorizes it appropriately.

[1106] Specific example

[1107] The user enters the following comments:

[1108] 1. "The project is progressing slowly."

[1109] 2. "There is a lack of communication."

[1110] 3. "We should introduce new tools."

[1111] 4. "The priorities of the tasks are unclear."

[1112] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[1113] Category 1: "Project Management"

[1114] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1115] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1116] Category 2: "Communication"

[1117] Problem: "Lack of communication" (Emotion: Confusion)

[1118] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[1119] Category 1: "Project Management"

[1120] assignment:

[1121] Progress is slow (emotion: dissatisfied)

[1122] Priorities are unclear (emotion: anxiety)

[1123] suggestion:

[1124] Introducing a new tool (emotion: suggestion)

[1125] Category 2: "Communication"

[1126] assignment:

[1127] Lack of communication (Emotion: Confusion)

[1128] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1129] Example of a prompt

[1130] The following are some examples of prompt statements that can be input to a generative AI model:

[1131] The user submitted the following feedback:

[1132] 1. The project is progressing slowly.

[1133] 2. Lack of communication

[1134] 3. New tools should be introduced.

[1135] 4. The priorities of the tasks are unclear.

[1136] Classify these opinions and feelings, and generate an agenda based on the main issues and suggestions. Feelings can be categorized as "dissatisfaction," "confusion," "suggestions," "anxiety," etc.

[1137] As described above, the system of the present invention is carried out in a series of steps, starting with opinion input, followed by opinion saving, classification, sentiment analysis, agenda generation, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[1138] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1139] Step 1:

[1140] Gathering opinions

[1141] Users enter their opinions into a dedicated UI form using smart glasses or a tablet. The opinion input form includes a text box and a submit button. Users enter their opinions into the text box and confirm their opinions by pressing the submit button. At this time, the input is text information, and the output is the text data of the entered opinion.

[1142] Step 2:

[1143] Submit your feedback

[1144] The terminal sends the input opinion text data to the server. The transmission is done using an HTTP POST request to the API endpoint. The input is the opinion text data obtained in step 1, and the output is the opinion data sent to the server.

[1145] Step 3:

[1146] Receiving and saving feedback

[1147] The server receives opinion data sent from the terminal. The server stores the received opinion data in a database. At this time, the input is the sent opinion data, and the output is the opinion data stored in the database. Once saving is complete, the server sends a "reception complete" message to the terminal.

[1148] Step 4:

[1149] Classification of opinions

[1150] The server extracts opinion data from the database and uses natural language processing (NLP) to classify the opinions into topics and categories. A generative AI model is used to automatically classify the opinions based on their content. In this process, the input is the opinion data extracted from the database, and the output is the classified result data.

[1151] Step 5:

[1152] sentiment analysis

[1153] The server performs sentiment analysis on the classified opinion data using an emotion engine. The emotion engine uses an AI model to extract sentiment data from the text and associate sentiment information with each opinion. In this process, the input is the classified opinion data, and the output is the opinion data with sentiment information added.

[1154] Step 6:

[1155] Agenda generation

[1156] The server extracts key issues and proposals based on categorized opinion data and sentiment information, and generates an agenda. In this process, the input is opinion data with added sentiment information, and the output is the generated agenda data. The agenda is organized by category and created in a flexible and effective format.

[1157] Step 7:

[1158] Agenda notification

[1159] The generated agenda is notified from the server to the user's terminal. This notification is sent via email, pop-up message, or other means. In this case, the input is the generated agenda data, and the output is the agenda notification received by the user. The user can then review this and efficiently proceed with their next action plan.

[1160] 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.

[1161] 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.

[1162] 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.

[1163] [Third Embodiment]

[1164] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1165] 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.

[1166] 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).

[1167] 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.

[1168] 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.

[1169] 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).

[1170] 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.

[1171] 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.

[1172] 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.

[1173] 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.

[1174] 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.

[1175] 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".

[1176] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system provides the following functions:

[1177] Gathering opinions

[1178] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[1179] Receiving and saving feedback

[1180] The server has an API endpoint for receiving feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[1181] Classification of opinions

[1182] The server classifies the received opinions using natural language processing (NLP). This processing is performed using generative AI or other appropriate algorithms. The purpose of classification is to divide the opinions into topics or categories. For example, the opinion "the project is progressing slowly" would be classified under the category "project management." The classification results are stored in a database.

[1183] Agenda generation

[1184] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. The generated agenda is stored in a database and formatted in a user-friendly format.

[1185] Agenda notification

[1186] The server sends the generated agenda to the user's device and notifies them. By referring to the agenda, the user can effectively utilize the results of the brainstorming session. When the notification is sent, information about the agenda will be displayed on the user's device via a pop-up message or email.

[1187] Specific example

[1188] For example, a user enters the following comment:

[1189] 1. "The project is progressing slowly."

[1190] 2. "There is a lack of communication."

[1191] 3. "We should introduce new tools."

[1192] 4. "The priorities of the tasks are unclear."

[1193] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[1194] Category 1: "Project Management"

[1195] Issues: "Slow progress," "Unclear priorities"

[1196] Proposal: "Introduction of new tools"

[1197] Category 2: "Communication"

[1198] Problem: "Lack of communication"

[1199] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[1200] Category 1: "Project Management"

[1201] assignment:

[1202] Slow progress

[1203] Priorities are unclear

[1204] suggestion:

[1205] Introducing new tools

[1206] Category 2: "Communication"

[1207] assignment:

[1208] There is a lack of communication.

[1209] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1210] As described above, the present invention is a system that can fairly and efficiently classify opinions from brainstorming sessions and compile important issues and proposals into an agenda.

[1211] The following describes the processing flow.

[1212] Program processing flow

[1213] Step 1: Enter and submit your comments.

[1214] Terminal:

[1215] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[1216] Specific actions:

[1217] 1. The user enters their opinion in the text box.

[1218] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[1219] Step 2: Receiving and saving feedback

[1220] server:

[1221] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[1222] Specific actions:

[1223] 1. The server receives a request to submit feedback from the terminal.

[1224] 2. Save the received feedback to the database.

[1225] 3. After saving is complete, a "Received" message will be sent to the device.

[1226] Step 3: Classification of Opinions

[1227] server:

[1228] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[1229] Specific actions:

[1230] 1. The server reads unclassified comments from the database.

[1231] 2. Pass the opinions to the NLP module and obtain the classification results.

[1232] 3. Re-save the categorized opinions in the database.

[1233] Step 4: Agenda Generation

[1234] server:

[1235] The server extracts key issues and proposals from the categorized opinions and generates an agenda. The agenda organizes the issues and proposals for each topic.

[1236] Specific actions:

[1237] 1. The server retrieves pre-classified opinions from the database.

[1238] 2. Identify the main issues and proposals for each category.

[1239] 3. Summarize the issues and proposals to create an agenda.

[1240] 4. Save the agenda to the database.

[1241] Step 5: Agenda Notification

[1242] server:

[1243] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[1244] Specific actions:

[1245] 1. The server reads the generated agenda from the database.

[1246] 2. Prepare the agenda in a format suitable for notifying users.

[1247] 3. Send the agenda to the user's device and notify them.

[1248] Step 6: User Verification

[1249] User:

[1250] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[1251] Specific actions:

[1252] 1. The user receives a notification and checks the agenda.

[1253] 2. Based on the agenda, formulate necessary plans and measures.

[1254] Thus, the system of the present invention is carried out in a series of steps, starting with user input of opinions, followed by saving, classifying, generating an agenda, notification, and user confirmation.

[1255] (Example 1)

[1256] 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."

[1257] In traditional brainstorming sessions, it was difficult to efficiently collect, classify, and organize the opinions entered by users. Furthermore, there was no method for quickly and accurately generating an agenda summarizing key issues and proposals. This resulted in problems such as insufficient organization of opinions and the inability to create effective agendas.

[1258] 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.

[1259] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, and means for transmitting and notifying the user of the agenda to the user's terminal. This makes it possible to effectively collect, classify, and organize opinions submitted by users in brainstorming sessions, and to quickly compile important issues and proposals into an agenda.

[1260] A "user" is someone who uses the system to input opinions and participate in brainstorming sessions.

[1261] "Opinions" refer to suggestions, issues, and information and feedback that users input during a brainstorming session.

[1262] "Means of collection" refers to mechanisms or devices for collecting and acquiring opinions entered by users.

[1263] "Transmission means" refers to methods or devices for sending collected opinions to a central processing unit.

[1264] A "central processing unit" is a computer system or server that receives opinions sent by users and processes them accordingly.

[1265] "Natural language processing" is a field of computer science that aims to mechanically analyze opinions and understand their meaning and intent.

[1266] A "classification method" refers to a method or device that uses natural language processing to organize received opinions into topics or categories.

[1267] "Extraction methods" refer to methods or devices for identifying and extracting important issues and proposals from classified opinions.

[1268] An "agenda generation method" refers to a method or device for organizing extracted issues and proposals and compiling them into an agenda.

[1269] "Notification means" refers to a method or device for sending the generated agenda to the user's terminal to inform them.

[1270] A "device" refers to a computer or mobile device that a user uses to input opinions and receive notifications.

[1271] A "database" is a storage location or system for saving and managing information such as received opinions, classification results, and agendas.

[1272] "Means of formatting" refers to methods or devices for arranging an agenda into a format that is easy for users to see and understand.

[1273] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system is implemented using the following hardware and software.

[1274] Users enter their opinions using a dedicated UI form on their device. For example, a web form built with HTML and CSS could be used, with JavaScript triggering a click event for the submit button. This form would include text boxes and a submit button.

[1275] The terminal sends the inputted opinion to the server. The server receives the request via an API endpoint (e.g., a REST API). The server uses a Python framework (such as Flask or Django) to set up the API endpoint, and upon receiving the opinion, it saves the opinion to a database (such as MySQL or PostgreSQL) using a Python ORM (Object-Relational Mapping). If the saving of the opinion is successful, the server returns a "Received" message to the terminal.

[1276] The server classifies the stored opinions using natural language processing (NLP). This analysis uses a generative AI model (e.g., GPT-3) or other appropriate algorithm. For example, a Python script is used to retrieve opinion data from the database and generate prompt sentences to input into the AI ​​model. The AI ​​model returns the classification results, which are then stored in the database.

[1277] Next, the server extracts key issues and suggestions from the categorized opinions and generates an agenda. In this process, the main issues and suggestions are organized within each category. The generated agenda is then formatted in a user-friendly format, for example, using a formatting library such as Markdown.

[1278] The server sends the generated agenda to the user's device and notifies them. Notifications are sent via pop-up messages or email. For example, notifications can be sent to the device using a REST API or WebSocket, and email notifications use the SMTP protocol. Users refer to the notified agenda and effectively utilize the results of the brainstorming session.

[1279] Specific example

[1280] For example, suppose a user enters feedback such as "Project progress is slow," "There is a lack of communication," "A new tool should be introduced," or "The work priorities are unclear" into a terminal and presses the send button. The terminal sends these feedback to a server, which stores them in a database. The server then uses natural language processing to classify the stored feedback as follows:

[1281] Category 1: "Project Management"

[1282] Issues: "Slow progress," "Unclear priorities"

[1283] Proposal: "Introduction of new tools"

[1284] Category 2: "Communication"

[1285] Problem: "Lack of communication"

[1286] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[1287] Category 1: "Project Management"

[1288] assignment:

[1289] Slow progress

[1290] Priorities are unclear

[1291] suggestion:

[1292] Introducing new tools

[1293] Category 2: "Communication"

[1294] assignment:

[1295] There is a lack of communication.

[1296] Examples of prompt statements

[1297] The following are specific examples of prompt statements to be input into a generative AI model:

[1298] The following are the opinions submitted by users during the brainstorming session.

[1299] 1. The project is progressing slowly.

[1300] 2. Lack of communication

[1301] 3. New tools should be introduced.

[1302] 4. The priorities of the tasks are unclear.

[1303] Please categorize these opinions and list the issues and suggestions for each category.

[1304] The present invention is a system that can effectively collect, classify, and organize the opinions submitted by users as described above, and quickly compile important issues and proposals into an agenda.

[1305] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1306] Step 1:

[1307] The user enters their opinion. The user uses a dedicated UI form on their device (built with HTML and CSS) to enter their opinion. For example, they might enter "Project progress is slow" and click the "Submit" button. The input data is in text format.

[1308] Specific actions:

[1309] The device uses JavaScript to detect the click event of the submit button and prepares the feedback as submission data.

[1310] Step 2:

[1311] The terminal sends the inputted opinion to the server. The terminal sends the opinion data as an HTTP request to the server's API endpoint.

[1312] Input: User-submitted opinions

[1313] Output: Opinion data sent to the server

[1314] Specific actions:

[1315] The device sends feedback to the server using JavaScript's Fetch API or XMLHttpRequest.

[1316] Step 3:

[1317] The server receives the feedback and saves it to a database. The server receives feedback data via an API endpoint (for example, using the Flask or Django framework in Python) and saves it to a database (MySQL or PostgreSQL). If the save is successful, the server returns a "received" message to the terminal.

[1318] Input: Opinion data sent from the device

[1319] Output: Opinion data stored in the database, "Received" message

[1320] Specific actions:

[1321] The server receives opinion data using an API built with Flask or Django and stores it in a database using an ORM.

[1322] Step 4:

[1323] The server analyzes and classifies the stored opinions using natural language processing (NLP). The server generates prompt sentences to input the stored opinion data into a generating AI model (e.g., GPT-3). The model classifies the opinions into categories (e.g., "project management" or "communication") and returns the results. The classification results are stored in a database.

[1324] Input: Opinion data stored in the database

[1325] Output: Classified opinion data, classification results

[1326] Specific actions:

[1327] The server uses a Python script to acquire opinion data and create prompt statements to input into the generative AI model.

[1328] The classification results obtained from the AI ​​model are stored in a database.

[1329] Step 5:

[1330] The server extracts key issues and suggestions from categorized opinions and generates an agenda. It organizes the main issues and suggestions by category and creates the agenda using a formatting library such as Markdown. This agenda is then stored in a database.

[1331] Input: Classified opinion data

[1332] Output: Generated agenda

[1333] Specific actions:

[1334] The server uses a Python script to process the classification results and extract issues and suggestions.

[1335] Format the agenda using a formatting library and save it to the database.

[1336] Step 6:

[1337] The server sends the generated agenda to the user's device and notifies them. This notification is delivered via a pop-up message or email. This allows the user to view the agenda.

[1338] Input: Generated agenda

[1339] Output: Agenda notification displayed on the user's device.

[1340] Specific actions:

[1341] The server sends the agenda to the terminal using a REST API or WebSocket.

[1342] For email notifications, emails are sent using SMTP.

[1343] The above processing steps enable the effective collection, classification, and organization of user input, allowing for the rapid compilation of important issues and suggestions into an agenda.

[1344] (Application Example 1)

[1345] 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."

[1346] In industrial environments, there is a need for methods to effectively collect worker opinions and improvement suggestions, appropriately classify and organize them, and summarize important issues and proposals. Furthermore, an efficient system is required to quickly notify managers of the collected opinions and implement them as improvement plans.

[1347] 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.

[1348] In this invention, the server includes means for collecting user inputs, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting the agenda to the user's terminal and notifying them, means for industrial workers to input their opinions into an interface, and means for collecting opinions via factory robots and transmitting them to the server. This makes it possible to effectively collect opinions from workers, quickly classify and organize them, and notify administrators.

[1349] (Definition of terms)

[1350] A "user" is a person, such as a worker or manager, who uses this system to input opinions or suggestions.

[1351] "Feedback" refers to improvement suggestions, reports, and comments on issues entered by users.

[1352] "Means of collection" refer to devices such as interfaces and sensors that electronically capture opinions entered by users.

[1353] "Means of transmission" refers to the process or device for sending collected opinions to a central processing unit via a communication network.

[1354] A "central processing unit" is a computer system or server used to process incoming opinions.

[1355] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[1356] "Classification methods" refer to processes or devices that divide opinions collected using natural language processing into specific categories or topics.

[1357] A "problem" is a problem raised by a user that requires a solution.

[1358] A "proposal" is a solution or improvement suggested by a user to address a problem.

[1359] An "agenda" is a list that organizes the issues and proposals extracted from categorized opinions.

[1360] "Means of notification" refers to processes or devices for informing users' devices of the generated agenda.

[1361] "Industrial environment workers" are employees who perform work in industrial facilities such as factories.

[1362] An "interface" refers to a device or software used by workers to input their opinions.

[1363] A "factory robot" is an automated device that performs tasks automatically within a factory and collects feedback.

[1364] A "database" is an information storage system used to store and manage received opinions and generated agendas.

[1365] overview

[1366] This invention is a system that collects opinions and suggestions from workers in an industrial environment, classifies them using natural language processing (NLP), extracts and organizes important issues and helpful suggestions, and generates an agenda. The generated agenda is notified to the terminals of workers and managers and used for rapid decision-making and improvement activities.

[1367] Hardware and software to be used

[1368] 1. Hardware

[1369] Smartphone: An interface for workers to input their opinions.

[1370] Factory robots: These robots have the function of collecting opinions from the industrial environment and sending them to a server.

[1371] Server: A central processing unit that receives, classifies, and generates agendas for opinions.

[1372] 2. Software

[1373] Python: A programming language used to write the main parts of a program.

[1374] Flask: A web framework for building API servers.

[1375] Spacy: A library for performing natural language processing (NLP).

[1376] SQLite: A database management system.

[1377] Processing flow

[1378] 1. Gathering opinions

[1379] Workers input their opinions using a smartphone or an interface mounted on a factory robot. The interface includes text boxes and a submit button.

[1380] The worker enters their opinion and presses the submit button, which sends the opinion to the server.

[1381] 2. Receiving and saving feedback

[1382] The server receives feedback via an API endpoint built using Flask. The received feedback is immediately saved to an SQLite database.

[1383] The server sends a "Received" message to the worker's terminal to confirm that the feedback was sent correctly.

[1384] 3. Classification of Opinions

[1385] The server uses Spacy to analyze the received comments using natural language processing and classify them into specific topics or categories. For example, the comment "The project is progressing slowly" would be classified under the category "Project Management."

[1386] The classification results are stored in the database.

[1387] 4. Agenda Generation

[1388] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the key issues and corresponding proposals are organized for each category.

[1389] The generated agenda is saved in the database.

[1390] 5. Agenda notification

[1391] The server sends the generated agenda to the terminals of workers and administrators, notifying them. Upon notification, the terminals display information about the agenda via pop-up messages or email.

[1392] Specific example

[1393] The worker enters the following comments:

[1394] 1. "The project is progressing slowly."

[1395] 2. "There is a lack of communication."

[1396] 3. "We should introduce new tools."

[1397] 4. "The priorities of the tasks are unclear."

[1398] These opinions are sent to the server's API endpoint and stored in a database. The server uses Spacy to categorize these opinions as follows:

[1399] Category 1: "Project Management"

[1400] Issues: "Slow progress," "Unclear priorities"

[1401] Proposal: "Introduction of new tools"

[1402] Category 2: "Communication"

[1403] Problem: "Lack of communication"

[1404] Next, the server generates an agenda from the classified results and sends an agenda like the following to the terminals of workers and administrators:

[1405] Category 1: "Project Management"

[1406] assignment:

[1407] Slow progress

[1408] Priorities are unclear

[1409] suggestion:

[1410] Introducing new tools

[1411] Category 2: "Communication"

[1412] assignment:

[1413] There is a lack of communication.

[1414] Example of a prompt:

[1415] "The opinion that progress is slow should be categorized under project management."

[1416] "The opinion that there is a lack of communication should be categorized under communication."

[1417] This makes it possible to effectively collect feedback from workers, quickly classify and organize it, and notify managers.

[1418] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1419] (Program processing flow)

[1420] Processing steps

[1421] Step 1:

[1422] Gathering opinions

[1423] Input: Users input their opinions into a smartphone or factory robot interface.

[1424] Operation: The user enters their opinion into a text box on the interface and presses the submit button. The opinion is structured as an HTTP request.

[1425] Output: Opinion data sent to the server.

[1426] Specific example: A worker types and sends the message, "The project is progressing slowly."

[1427] Step 2:

[1428] Receiving and saving feedback

[1429] Input: The server receives an HTTP request.

[1430] Operation: The server uses Flask to receive feedback at the API endpoint and saves the request data to an SQLite database.

[1431] Output: Saved opinion data and a message confirming receipt.

[1432] Specific example: The Flask API saves the feedback "Project progress is slow" to a database and sends a message to the user's terminal confirming receipt.

[1433] Step 3:

[1434] Classification of opinions

[1435] Input: Received opinion data.

[1436] Operation: The server uses Spacy to analyze opinions and classify them into specific topics or categories using natural language processing. This includes text tokenization and entity recognition.

[1437] Output: Classified opinion data.

[1438] Specific example: The opinion that "progress is slow" is categorized under "project management."

[1439] Step 4:

[1440] Agenda generation

[1441] Input: Classified opinion data.

[1442] Operation: The server extracts key issues and proposals based on the classification results and generates an agenda for each category. It organizes the text of the issues and proposals and formats them into an easy-to-read format.

[1443] Output: Agenda data.

[1444] Specific example: An agenda is generated in the "Project Management" category that combines the issue of "slow progress" and the suggestion of "introducing a new tool."

[1445] Step 5:

[1446] Agenda notification

[1447] Input: Generated agenda data.

[1448] Operation: The server sends the agenda to the user's device and sends a notification. Information about the agenda is displayed via a pop-up message or email.

[1449] Output: Agenda notification displayed on the user's terminal.

[1450] Specific example: The administrator's smartphone receives a pop-up message notification for an agenda item in the "Project Management" category.

[1451] (Example of a prompt message)

[1452] "The opinion that progress is slow should be categorized under project management."

[1453] "The opinion that there is a lack of communication should be categorized under communication."

[1454] 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.

[1455] One embodiment of the present invention combines a system that effectively collects, classifies, and organizes user input, and summarizes important issues and proposals as an agenda, with an emotion engine that recognizes the user's emotions. This system provides the following functions:

[1456] Gathering opinions

[1457] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[1458] Receiving and saving feedback

[1459] The server has an API endpoint that receives feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[1460] Classification of Opinions and Sentiment Analysis

[1461] 1. Classification of opinions:

[1462] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[1463] 2. Sentiment analysis:

[1464] The server then uses an emotion engine to analyze the emotions contained in the user's comments. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each comment.

[1465] The analysis results are saved back into the database. Based on this sentiment information, the accuracy of opinion classification improves, enabling classifications that reflect the user's intentions and emotions.

[1466] Agenda generation

[1467] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. Furthermore, a flexible agenda is created that takes sentiment information into account.

[1468] The generated agenda is saved in the database and formatted in a user-friendly format.

[1469] Agenda notification

[1470] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[1471] Specific example

[1472] The user enters the following comments:

[1473] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[1474] 2. "Lack of communication" (Emotion: Confusion)

[1475] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[1476] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[1477] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[1478] Category 1: "Project Management"

[1479] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1480] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1481] Category 2: "Communication"

[1482] Problem: "Lack of communication" (Emotion: Confusion)

[1483] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[1484] Category 1: "Project Management"

[1485] assignment:

[1486] Progress is slow (emotion: dissatisfied)

[1487] Priorities are unclear (emotion: anxiety)

[1488] suggestion:

[1489] Introducing a new tool (emotion: suggestion)

[1490] Category 2: "Communication"

[1491] assignment:

[1492] Lack of communication (Emotion: Confusion)

[1493] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1494] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[1495] The following describes the processing flow.

[1496] Program processing flow

[1497] Step 1: Enter and submit your comments.

[1498] Terminal:

[1499] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[1500] Specific actions:

[1501] 1. The user enters their opinion in the text box.

[1502] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[1503] Step 2: Receiving and saving feedback

[1504] server:

[1505] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[1506] Specific actions:

[1507] 1. The server receives a request to submit feedback from the terminal.

[1508] 2. Save the received feedback to the database.

[1509] 3. After saving is complete, a "Received" message will be sent to the device.

[1510] Step 3: Classifying opinions and analyzing sentiment

[1511] server:

[1512] The server extracts opinions from the database and classifies them using natural language processing (NLP). It also uses an emotion engine to recognize the emotions contained in the opinions.

[1513] Specific actions:

[1514] 1. The server reads unclassified comments from the database.

[1515] 2. Pass the opinions to a natural language processing module and classify them by topic.

[1516] 3. Pass the categorized opinions to the sentiment engine to generate sentiment data.

[1517] 4. Re-save the emotion data and classification results to the database.

[1518] Step 4: Agenda Generation

[1519] server:

[1520] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. The agenda organizes the issues and proposals for each topic along with sentiment information.

[1521] Specific actions:

[1522] 1. The server retrieves pre-classified opinion and sentiment data from the database.

[1523] 2. Extract the main issues and proposals, as well as related sentiment information, for each category.

[1524] 3. Summarize the issues and proposals to create an agenda.

[1525] 4. Save the agenda to the database.

[1526] Step 5: Agenda Notification

[1527] server:

[1528] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[1529] Specific actions:

[1530] 1. The server reads the generated agenda from the database.

[1531] 2. Prepare the agenda in a format suitable for notifying users.

[1532] 3. Send the agenda to the user's device and notify them.

[1533] Step 6: User Verification

[1534] User:

[1535] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[1536] Specific actions:

[1537] 1. The user receives a notification and checks the agenda.

[1538] 2. Based on the agenda, formulate necessary plans and measures.

[1539] Specific example

[1540] The user enters the following comments:

[1541] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[1542] 2. "Lack of communication" (Emotion: Confusion)

[1543] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[1544] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[1545] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[1546] Category 1: "Project Management"

[1547] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1548] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1549] Category 2: "Communication"

[1550] Problem: "Lack of communication" (Emotion: Confusion)

[1551] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[1552] Category 1: "Project Management"

[1553] assignment:

[1554] Progress is slow (emotion: dissatisfied)

[1555] Priorities are unclear (emotion: anxiety)

[1556] suggestion:

[1557] Introducing a new tool (emotion: suggestion)

[1558] Category 2: "Communication"

[1559] assignment:

[1560] Lack of communication (Emotion: Confusion)

[1561] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1562] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[1563] (Example 2)

[1564] 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."

[1565] Traditional opinion gathering systems simply collect user input without adequately understanding their intentions and emotions, making it difficult to generate effective agendas. Furthermore, the lack of emotion-based classification and analysis made it challenging to extract specific issues and proposals that reflected user intent. This resulted in insufficient processing of user feedback, hindering efficient meetings and the development of action plans.

[1566] 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.

[1567] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions using natural language processing, means for analyzing the emotions contained in the opinions, means for extracting issues and proposals from the classified opinions and emotion information and generating an agenda, and means for transmitting and notifying the user of the agenda at their terminal. This enables the generation of a flexible and effective agenda that reflects the user's intentions and emotions.

[1568] A "user" is a person who operates this system and inputs their opinions.

[1569] "Opinions" refer to text data that users input for collection.

[1570] "Means of collection" refers to the interface of a device equipped with a UI form or text box for users to input their opinions.

[1571] "Means of transmission" refers to the function of a terminal that has a communication function for transmitting user input to a central processing unit.

[1572] A "central processing unit" is an information processing system, such as a server or computer, that performs tasks like classifying opinions and analyzing sentiment.

[1573] "Natural language processing" is a technology that enables computers to understand, interpret, and process human language.

[1574] "Means of classification" refer to algorithms and generative AI models that use natural language processing to divide opinions into topics or categories.

[1575] "Means of analyzing emotions" refer to emotion engines and algorithms used to analyze the emotions of users contained in their opinions.

[1576] "Emotional information" refers to emotional data extracted from analyzed opinions.

[1577] A "problem" is an issue extracted from classified opinion and sentiment information.

[1578] A "proposal" is an idea or method extracted as a solution to a problem.

[1579] An "agenda" is a plan for a meeting or action plan generated based on categorized opinions and sentiment information.

[1580] "Means of notification" refers to communication methods such as email or pop-up messages used to convey the generated agenda to the user.

[1581] This invention employs a system that effectively collects, classifies, and organizes user input, summarizing important issues and proposals as an agenda, and combines this with an emotion engine that recognizes the user's emotions. The following hardware and software are used to implement this system.

[1582] Hardware to use:

[1583] 1. Device: A device used to input opinions (such as a personal computer or smartphone)

[1584] 2. Server: A computer system for data processing and storage, classification, and sentiment analysis.

[1585] Software to use:

[1586] 1. UI Form: An interface for users to input their opinions.

[1587] 2. API Endpoint: A means of communication for sending feedback from a device to a server.

[1588] 3. Database: A management system for storing opinions and analysis results (e.g., MySQL, PostgreSQL)

[1589] 4. Natural Language Processing Systems: NLP tools and algorithms for classifying opinions (e.g., BERT, generative AI models)

[1590] 5. Emotion Engine: A tool that analyzes the emotions contained in user feedback (e.g., IBM Watson Natural Language Understanding, Microsoft Text Analytics API)

[1591] 6. Notification System: Means for notifying users of the agenda (e.g., email, WebSockets, push notifications)

[1592] Program processing

[1593] Users utilize a UI form on their device to input their opinions during brainstorming sessions. When a user enters their opinion into a text box and presses the "Submit" button, the opinion is sent to the server's API endpoint via an HTTP POST request.

[1594] The server receives this opinion and immediately saves it to the database. Once saving is complete, a "Received" message is sent to the terminal. The server then extracts the saved opinion from the database and uses natural language processing (NLP) to classify the opinion into topics and categories.

[1595] Next, the server uses an emotion engine to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. Based on this emotion information, the accuracy of opinion classification is improved, enabling classification that reflects the user's intentions and emotions.

[1596] Next, the server extracts key issues and suggestions based on the categorized opinions and sentiment information, and generates an agenda. This agenda organizes the main issues and suggestions for each category and is formatted in a flexible format. The generated agenda is saved in the database and notified to the user. Email and pop-up messages are often used as notification methods.

[1597] Specific example

[1598] Suppose a user enters the following opinion into their device and presses the submit button:

[1599] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[1600] 2. "Lack of communication" (Emotion: Confusion)

[1601] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[1602] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[1603] The server receives these opinions and stores them in a database. It then uses a natural language processing system to classify the opinions as follows:

[1604] Category 1: "Project Management"

[1605] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1606] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1607] Category 2: "Communication"

[1608] Problem: "Lack of communication" (Emotion: Confusion)

[1609] Next, taking emotional information into consideration, the server generates an agenda based on key issues and suggestions and sends it to the user as follows:

[1610] Category 1: "Project Management"

[1611] assignment:

[1612] Progress is slow (emotion: dissatisfied)

[1613] Priorities are unclear (emotion: anxiety)

[1614] suggestion:

[1615] Introducing a new tool (emotion: suggestion)

[1616] Category 2: "Communication"

[1617] assignment:

[1618] Lack of communication (Emotion: Confusion)

[1619] Example of a prompt

[1620] "Please categorize the following opinions and perform a sentiment analysis: 'Project progress is slow,' 'Lack of communication,' 'New tools should be introduced,' 'Work priorities are unclear.'"

[1621] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1622] Step 1:

[1623] The user enters their opinion.

[1624] Users input their opinions during brainstorming sessions using a dedicated UI form on their device. Specifically, they enter their opinions in a text box and press the "Submit" button. For example, they might input the opinion, "The project is progressing slowly." Input: User's opinion (text). Output: User's opinion (text).

[1625] Step 2:

[1626] The device sends the feedback.

[1627] When the user presses the submit button, the device sends the feedback to the server's API endpoint in the form of an HTTP POST request. The submitted data includes the text of the feedback entered by the user. Input: User's feedback (text). Output: Feedback data sent to the server (HTTP request).

[1628] Step 3:

[1629] The server receives and saves the feedback.

[1630] The server receives an HTTP POST request from the terminal, extracts the opinion data, and saves it to the database. MySQL, PostgreSQL, or other databases can be used for storage, depending on data integrity. Once saving is complete, the server sends a "reception complete" message to the terminal. Input: Sent opinion data (HTTP request). Output: Saved opinion data (database), reception complete message (HTTP response).

[1631] Step 4:

[1632] The server categorizes the opinions.

[1633] The server extracts opinion data stored in the database and classifies the opinions using natural language processing (NLP). Specifically, it uses algorithms such as BERT and generative AI models to classify opinions into topics and categories. For example, the opinion "the project is progressing slowly" would be classified under "project management." Input: Opinion data extracted from the database. Output: Classification results (topics and categories).

[1634] Step 5:

[1635] The server analyzes emotions.

[1636] The server uses an emotion engine to analyze the emotions contained in the classified opinion data. For example, it may utilize IBM Watson Natural Language Understanding or the Microsoft Text Analytics API. The emotion engine extracts emotion data (e.g., dissatisfaction, joy, suggestion, etc.) from the opinion text and generates emotion information corresponding to each opinion. Input: Classified opinion data. Output: Emotion information (extracted emotion data).

[1637] Step 6:

[1638] The server generates the agenda.

[1639] Based on classification results and sentiment information, the server extracts key issues and proposals and creates an agenda. For example, it organizes the main issues and corresponding proposals for each category and generates a flexible agenda that reflects sentiment information. Input: Classification results, sentiment information. Output: Generated agenda (list of issues and proposals).

[1640] Step 7:

[1641] The server notifies the user of the agenda.

[1642] The generated agenda is notified to the user's device. Notification methods include email and pop-up messages. SMTP servers, WebSockets, and push notification services can be used. For example, the generated agenda can be sent via email, with the content provided as a URL link. Input: Generated agenda. Output: Notification message sent to the user (email, pop-up notification).

[1643] (Application Example 2)

[1644] 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."

[1645] In factory production lines, there is a lack of efficient means to collect worker feedback and use it to improve work efficiency. Furthermore, because the emotions contained in worker feedback are not considered, the accuracy and effectiveness of improvement suggestions may decrease. To address this challenge, a system is needed that effectively collects and analyzes user feedback and emotions, and generates and notifies appropriate agendas.

[1646] 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 opinions input by the user, means for transmitting the opinions to a central processing device, means for classifying the opinions in the central processing device using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting and notifying the user of the agenda to the user's terminal, means for generating emotion information using an emotion engine that recognizes the emotions contained in the user's opinions, and means for improving the accuracy of agenda generation based on the emotion information. This makes it possible to effectively collect and analyze the opinions and emotions of workers and create and notify them of an agenda that includes specific points for improvement.

[1647] A "user" is an entity that uses the system to input opinions and receives agenda generation and notifications.

[1648] "Opinions" refer to text information entered by users through the system, and include issues and suggestions.

[1649] A "central processing unit" is a device that functions as a server, receiving, classifying, and generating an agenda based on opinions submitted by users.

[1650] "Natural language processing" is a technique used by central processing units to classify opinions into topics and categories, and is a means of analyzing text data.

[1651] An "emotion engine" is a technology or tool that recognizes the emotions contained in a user's opinion and generates emotional information.

[1652] An "agenda" is a compilation of issues and proposals extracted from opinions, presented in a format that is easy for users to understand.

[1653] A "database" is an information storage system for saving received opinions and generated sentiment information and agendas.

[1654] "Notification" refers to the act of informing a user's device of a generated agenda, and includes methods such as email and pop-up messages.

[1655] "Emotional information" refers to emotional data contained in an opinion, generated by an emotion engine.

[1656] One embodiment of the present invention relates to a system for collecting and analyzing the opinions and feelings of workers on a factory production line to generate an efficient agenda. This system is implemented with the following configuration and procedure.

[1657] System Configuration

[1658] 1. Gathering opinions

[1659] Users (workers) input their opinions using smart glasses or tablets. The input opinions are sent to the central processing unit (server) via a dedicated UI form.

[1660] 2. Receiving and saving feedback

[1661] The server receives the feedback sent from the device via the API endpoint and stores it in the database. The server verifies that the feedback has been saved correctly and sends a "Received" message to the device.

[1662] 3. Classification of Opinions and Sentiment Analysis

[1663] The server extracts opinions from the database and uses natural language processing (NLP) to categorize them into topics and categories. It uses a generative AI model to apply an algorithm that automatically categorizes the opinions.

[1664] Next, an emotion engine is used to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. This improves the accuracy of opinion classification and enables classification that reflects the user's intentions and emotions.

[1665] 4. Agenda Generation

[1666] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. This agenda is organized by category and created in a flexible and effective format.

[1667] 5. Agenda notification

[1668] The generated agenda is sent from the server to the user's device. The notification is sent via methods such as email or a pop-up message, allowing the user to review it and efficiently proceed with their next action plan.

[1669] Program processing

[1670] The server uses the following hardware and software:

[1671] Hardware: Factory robots, smart devices (smart glasses, tablets).

[1672] Software: Python, Node.js, MongoDB, React Native.

[1673] The server classifies opinions using natural language processing (NLP) and utilizes a generative AI model such as OpenAI's GPT-3. Similarly, the emotion engine extracts emotion data using an AI model and categorizes it appropriately.

[1674] Specific example

[1675] The user enters the following comments:

[1676] 1. "The project is progressing slowly."

[1677] 2. "There is a lack of communication."

[1678] 3. "We should introduce new tools."

[1679] 4. "The priorities of the tasks are unclear."

[1680] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[1681] Category 1: "Project Management"

[1682] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[1683] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[1684] Category 2: "Communication"

[1685] Problem: "Lack of communication" (Emotion: Confusion)

[1686] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[1687] Category 1: "Project Management"

[1688] assignment:

[1689] Progress is slow (emotion: dissatisfied)

[1690] Priorities are unclear (emotion: anxiety)

[1691] suggestion:

[1692] Introducing a new tool (emotion: suggestion)

[1693] Category 2: "Communication"

[1694] assignment:

[1695] Lack of communication (Emotion: Confusion)

[1696] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1697] Example of a prompt

[1698] The following are some examples of prompt statements that can be input to a generative AI model:

[1699] The user submitted the following feedback:

[1700] 1. The project is progressing slowly.

[1701] 2. Lack of communication

[1702] 3. New tools should be introduced.

[1703] 4. The priorities of the tasks are unclear.

[1704] Classify these opinions and feelings, and generate an agenda based on the main issues and suggestions. Feelings can be categorized as "dissatisfaction," "confusion," "suggestions," "anxiety," etc.

[1705] As described above, the system of the present invention is carried out in a series of steps, starting with opinion input, followed by opinion saving, classification, sentiment analysis, agenda generation, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[1706] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1707] Step 1:

[1708] Gathering opinions

[1709] Users enter their opinions into a dedicated UI form using smart glasses or a tablet. The opinion input form includes a text box and a submit button. Users enter their opinions into the text box and confirm their opinions by pressing the submit button. At this time, the input is text information, and the output is the text data of the entered opinion.

[1710] Step 2:

[1711] Submit your feedback

[1712] The terminal sends the input opinion text data to the server. The transmission is done using an HTTP POST request to the API endpoint. The input is the opinion text data obtained in step 1, and the output is the opinion data sent to the server.

[1713] Step 3:

[1714] Receiving and saving feedback

[1715] The server receives opinion data sent from the terminal. The server stores the received opinion data in a database. At this time, the input is the sent opinion data, and the output is the opinion data stored in the database. Once saving is complete, the server sends a "reception complete" message to the terminal.

[1716] Step 4:

[1717] Classification of opinions

[1718] The server extracts opinion data from the database and uses natural language processing (NLP) to classify the opinions into topics and categories. A generative AI model is used to automatically classify the opinions based on their content. In this process, the input is the opinion data extracted from the database, and the output is the classified result data.

[1719] Step 5:

[1720] sentiment analysis

[1721] The server performs sentiment analysis on the classified opinion data using an emotion engine. The emotion engine uses an AI model to extract sentiment data from the text and associate sentiment information with each opinion. In this process, the input is the classified opinion data, and the output is the opinion data with sentiment information added.

[1722] Step 6:

[1723] Agenda generation

[1724] The server extracts key issues and proposals based on categorized opinion data and sentiment information, and generates an agenda. In this process, the input is opinion data with added sentiment information, and the output is the generated agenda data. The agenda is organized by category and created in a flexible and effective format.

[1725] Step 7:

[1726] Agenda notification

[1727] The generated agenda is notified from the server to the user's terminal. This notification is sent via email, pop-up message, or other means. In this case, the input is the generated agenda data, and the output is the agenda notification received by the user. The user can then review this and efficiently proceed with their next action plan.

[1728] 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.

[1729] 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.

[1730] 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.

[1731] [Fourth Embodiment]

[1732] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1733] 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.

[1734] 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).

[1735] 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.

[1736] 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.

[1737] 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).

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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".

[1745] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system provides the following functions:

[1746] Gathering opinions

[1747] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[1748] Receiving and saving feedback

[1749] The server has an API endpoint for receiving feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[1750] Classification of opinions

[1751] The server classifies the received opinions using natural language processing (NLP). This processing is performed using generative AI or other appropriate algorithms. The purpose of classification is to divide the opinions into topics or categories. For example, the opinion "the project is progressing slowly" would be classified under the category "project management." The classification results are stored in a database.

[1752] Agenda generation

[1753] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. The generated agenda is stored in a database and formatted in a user-friendly format.

[1754] Agenda notification

[1755] The server sends the generated agenda to the user's device and notifies them. By referring to the agenda, the user can effectively utilize the results of the brainstorming session. When the notification is sent, information about the agenda will be displayed on the user's device via a pop-up message or email.

[1756] Specific example

[1757] For example, a user enters the following comment:

[1758] 1. "The project is progressing slowly."

[1759] 2. "There is a lack of communication."

[1760] 3. "We should introduce new tools."

[1761] 4. "The priorities of the tasks are unclear."

[1762] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[1763] Category 1: "Project Management"

[1764] Issues: "Slow progress," "Unclear priorities"

[1765] Proposal: "Introduction of new tools"

[1766] Category 2: "Communication"

[1767] Problem: "Lack of communication"

[1768] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[1769] Category 1: "Project Management"

[1770] assignment:

[1771] Slow progress

[1772] Priorities are unclear

[1773] suggestion:

[1774] Introducing new tools

[1775] Category 2: "Communication"

[1776] assignment:

[1777] There is a lack of communication.

[1778] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[1779] As described above, the present invention is a system that can fairly and efficiently classify opinions from brainstorming sessions and compile important issues and proposals into an agenda.

[1780] The following describes the processing flow.

[1781] Program processing flow

[1782] Step 1: Enter and submit your comments.

[1783] Terminal:

[1784] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[1785] Specific actions:

[1786] 1. The user enters their opinion in the text box.

[1787] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[1788] Step 2: Receiving and saving feedback

[1789] server:

[1790] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[1791] Specific actions:

[1792] 1. The server receives a request to submit feedback from the terminal.

[1793] 2. Save the received feedback to the database.

[1794] 3. After saving is complete, a "Received" message will be sent to the device.

[1795] Step 3: Classification of Opinions

[1796] server:

[1797] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[1798] Specific actions:

[1799] 1. The server reads unclassified comments from the database.

[1800] 2. Pass the opinions to the NLP module and obtain the classification results.

[1801] 3. Re-save the categorized opinions in the database.

[1802] Step 4: Agenda Generation

[1803] server:

[1804] The server extracts key issues and proposals from the categorized opinions and generates an agenda. The agenda organizes the issues and proposals for each topic.

[1805] Specific actions:

[1806] 1. The server retrieves pre-classified opinions from the database.

[1807] 2. Identify the main issues and proposals for each category.

[1808] 3. Summarize the issues and proposals to create an agenda.

[1809] 4. Save the agenda to the database.

[1810] Step 5: Agenda Notification

[1811] server:

[1812] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[1813] Specific actions:

[1814] 1. The server reads the generated agenda from the database.

[1815] 2. Prepare the agenda in a format suitable for notifying users.

[1816] 3. Send the agenda to the user's device and notify them.

[1817] Step 6: User Verification

[1818] User:

[1819] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[1820] Specific actions:

[1821] 1. The user receives a notification and checks the agenda.

[1822] 2. Based on the agenda, formulate necessary plans and measures.

[1823] Thus, the system of the present invention is carried out in a series of steps, starting with user input of opinions, followed by saving, classifying, generating an agenda, notification, and user confirmation.

[1824] (Example 1)

[1825] 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".

[1826] In traditional brainstorming sessions, it was difficult to efficiently collect, classify, and organize the opinions entered by users. Furthermore, there was no method for quickly and accurately generating an agenda summarizing key issues and proposals. This resulted in problems such as insufficient organization of opinions and the inability to create effective agendas.

[1827] 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.

[1828] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, and means for transmitting and notifying the user of the agenda to the user's terminal. This makes it possible to effectively collect, classify, and organize opinions submitted by users in brainstorming sessions, and to quickly compile important issues and proposals into an agenda.

[1829] A "user" is someone who uses the system to input opinions and participate in brainstorming sessions.

[1830] "Opinions" refer to suggestions, issues, and information and feedback that users input during a brainstorming session.

[1831] "Means of collection" refers to mechanisms or devices for collecting and acquiring opinions entered by users.

[1832] "Transmission means" refers to methods or devices for sending collected opinions to a central processing unit.

[1833] A "central processing unit" is a computer system or server that receives opinions sent by users and processes them accordingly.

[1834] "Natural language processing" is a field of computer science that aims to mechanically analyze opinions and understand their meaning and intent.

[1835] A "classification method" refers to a method or device that uses natural language processing to organize received opinions into topics or categories.

[1836] "Extraction methods" refer to methods or devices for identifying and extracting important issues and proposals from classified opinions.

[1837] An "agenda generation method" refers to a method or device for organizing extracted issues and proposals and compiling them into an agenda.

[1838] "Notification means" refers to a method or device for sending the generated agenda to the user's terminal to inform them.

[1839] A "device" refers to a computer or mobile device that a user uses to input opinions and receive notifications.

[1840] A "database" is a storage location or system for saving and managing information such as received opinions, classification results, and agendas.

[1841] "Means of formatting" refers to methods or devices for arranging an agenda into a format that is easy for users to see and understand.

[1842] The embodiment of the present invention relates to a system that effectively collects, classifies, and organizes user input, and compiles important issues and suggestions into an agenda. This system is implemented using the following hardware and software.

[1843] Users enter their opinions using a dedicated UI form on their device. For example, a web form built with HTML and CSS could be used, with JavaScript triggering a click event for the submit button. This form would include text boxes and a submit button.

[1844] The terminal sends the inputted opinion to the server. The server receives the request via an API endpoint (e.g., a REST API). The server uses a Python framework (such as Flask or Django) to set up the API endpoint, and upon receiving the opinion, it saves the opinion to a database (such as MySQL or PostgreSQL) using a Python ORM (Object-Relational Mapping). If the saving of the opinion is successful, the server returns a "Received" message to the terminal.

[1845] The server classifies the stored opinions using natural language processing (NLP). This analysis uses a generative AI model (e.g., GPT-3) or other appropriate algorithm. For example, a Python script is used to retrieve opinion data from the database and generate prompt sentences to input into the AI ​​model. The AI ​​model returns the classification results, which are then stored in the database.

[1846] Next, the server extracts key issues and suggestions from the categorized opinions and generates an agenda. In this process, the main issues and suggestions are organized within each category. The generated agenda is then formatted in a user-friendly format, for example, using a formatting library such as Markdown.

[1847] The server sends the generated agenda to the user's device and notifies them. Notifications are sent via pop-up messages or email. For example, notifications can be sent to the device using a REST API or WebSocket, and email notifications use the SMTP protocol. Users refer to the notified agenda and effectively utilize the results of the brainstorming session.

[1848] Specific example

[1849] For example, suppose a user enters feedback such as "Project progress is slow," "There is a lack of communication," "A new tool should be introduced," or "The work priorities are unclear" into a terminal and presses the send button. The terminal sends these feedback to a server, which stores them in a database. The server then uses natural language processing to classify the stored feedback as follows:

[1850] Category 1: "Project Management"

[1851] Issues: "Slow progress," "Unclear priorities"

[1852] Proposal: "Introduction of new tools"

[1853] Category 2: "Communication"

[1854] Problem: "Lack of communication"

[1855] Next, the server generates an agenda from the classified results and sends the following agenda to the user:

[1856] Category 1: "Project Management"

[1857] assignment:

[1858] Slow progress

[1859] Priorities are unclear

[1860] suggestion:

[1861] Introducing new tools

[1862] Category 2: "Communication"

[1863] assignment:

[1864] There is a lack of communication.

[1865] Examples of prompt statements

[1866] The following are specific examples of prompt statements to be input into a generative AI model:

[1867] The following are the opinions submitted by users during the brainstorming session.

[1868] 1. The project is progressing slowly.

[1869] 2. Lack of communication

[1870] 3. New tools should be introduced.

[1871] 4. The priorities of the tasks are unclear.

[1872] Please categorize these opinions and list the issues and suggestions for each category.

[1873] The present invention is a system that can effectively collect, classify, and organize the opinions submitted by users as described above, and quickly compile important issues and proposals into an agenda.

[1874] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1875] Step 1:

[1876] The user enters their opinion. The user uses a dedicated UI form on their device (built with HTML and CSS) to enter their opinion. For example, they might enter "Project progress is slow" and click the "Submit" button. The input data is in text format.

[1877] Specific actions:

[1878] The device uses JavaScript to detect the click event of the submit button and prepares the feedback as submission data.

[1879] Step 2:

[1880] The terminal sends the inputted opinion to the server. The terminal sends the opinion data as an HTTP request to the server's API endpoint.

[1881] Input: User-submitted opinions

[1882] Output: Opinion data sent to the server

[1883] Specific actions:

[1884] The device sends feedback to the server using JavaScript's Fetch API or XMLHttpRequest.

[1885] Step 3:

[1886] The server receives the feedback and saves it to a database. The server receives feedback data via an API endpoint (for example, using the Flask or Django framework in Python) and saves it to a database (MySQL or PostgreSQL). If the save is successful, the server returns a "received" message to the terminal.

[1887] Input: Opinion data sent from the device

[1888] Output: Opinion data stored in the database, "Received" message

[1889] Specific actions:

[1890] The server receives opinion data using an API built with Flask or Django and stores it in a database using an ORM.

[1891] Step 4:

[1892] The server analyzes and classifies the stored opinions using natural language processing (NLP). The server generates prompt sentences to input the stored opinion data into a generating AI model (e.g., GPT-3). The model classifies the opinions into categories (e.g., "project management" or "communication") and returns the results. The classification results are stored in a database.

[1893] Input: Opinion data stored in the database

[1894] Output: Classified opinion data, classification results

[1895] Specific actions:

[1896] The server uses a Python script to acquire opinion data and create prompt statements to input into the generative AI model.

[1897] The classification results obtained from the AI ​​model are stored in a database.

[1898] Step 5:

[1899] The server extracts key issues and suggestions from categorized opinions and generates an agenda. It organizes the main issues and suggestions by category and creates the agenda using a formatting library such as Markdown. This agenda is then stored in a database.

[1900] Input: Classified opinion data

[1901] Output: Generated agenda

[1902] Specific actions:

[1903] The server uses a Python script to process the classification results and extract issues and suggestions.

[1904] Format the agenda using a formatting library and save it to the database.

[1905] Step 6:

[1906] The server sends the generated agenda to the user's device and notifies them. This notification is delivered via a pop-up message or email. This allows the user to view the agenda.

[1907] Input: Generated agenda

[1908] Output: Agenda notification displayed on the user's device.

[1909] Specific actions:

[1910] The server sends the agenda to the terminal using a REST API or WebSocket.

[1911] For email notifications, emails are sent using SMTP.

[1912] The above processing steps enable the effective collection, classification, and organization of user input, allowing for the rapid compilation of important issues and suggestions into an agenda.

[1913] (Application Example 1)

[1914] 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".

[1915] In industrial environments, there is a need for methods to effectively collect worker opinions and improvement suggestions, appropriately classify and organize them, and summarize important issues and proposals. Furthermore, an efficient system is required to quickly notify managers of the collected opinions and implement them as improvement plans.

[1916] 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.

[1917] In this invention, the server includes means for collecting user inputs, means for transmitting the opinions to a central processing unit, means for classifying the opinions in the central processing unit using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting the agenda to the user's terminal and notifying them, means for industrial workers to input their opinions into an interface, and means for collecting opinions via factory robots and transmitting them to the server. This makes it possible to effectively collect opinions from workers, quickly classify and organize them, and notify administrators.

[1918] (Definition of terms)

[1919] A "user" is a person, such as a worker or manager, who uses this system to input opinions or suggestions.

[1920] "Feedback" refers to improvement suggestions, reports, and comments on issues entered by users.

[1921] "Means of collection" refer to devices such as interfaces and sensors that electronically capture opinions entered by users.

[1922] "Means of transmission" refers to the process or device for sending collected opinions to a central processing unit via a communication network.

[1923] A "central processing unit" is a computer system or server used to process incoming opinions.

[1924] "Natural language processing" is a technology that enables computers to understand and analyze human language.

[1925] "Classification methods" refer to processes or devices that divide opinions collected using natural language processing into specific categories or topics.

[1926] A "problem" is a problem raised by a user that requires a solution.

[1927] A "proposal" is a solution or improvement suggested by a user to address a problem.

[1928] An "agenda" is a list that organizes the issues and proposals extracted from categorized opinions.

[1929] "Means of notification" refers to processes or devices for informing users' devices of the generated agenda.

[1930] "Industrial environment workers" are employees who perform work in industrial facilities such as factories.

[1931] An "interface" refers to a device or software used by workers to input their opinions.

[1932] A "factory robot" is an automated device that performs tasks automatically within a factory and collects feedback.

[1933] A "database" is an information storage system used to store and manage received opinions and generated agendas.

[1934] overview

[1935] This invention is a system that collects opinions and suggestions from workers in an industrial environment, classifies them using natural language processing (NLP), extracts and organizes important issues and helpful suggestions, and generates an agenda. The generated agenda is notified to the terminals of workers and managers and used for rapid decision-making and improvement activities.

[1936] Hardware and software to be used

[1937] 1. Hardware

[1938] Smartphone: An interface for workers to input their opinions.

[1939] Factory robots: These robots have the function of collecting opinions from the industrial environment and sending them to a server.

[1940] Server: A central processing unit that receives, classifies, and generates agendas for opinions.

[1941] 2. Software

[1942] Python: A programming language used to write the main parts of a program.

[1943] Flask: A web framework for building API servers.

[1944] Spacy: A library for performing natural language processing (NLP).

[1945] SQLite: A database management system.

[1946] Processing flow

[1947] 1. Gathering opinions

[1948] Workers input their opinions using a smartphone or an interface mounted on a factory robot. The interface includes text boxes and a submit button.

[1949] The worker enters their opinion and presses the submit button, which sends the opinion to the server.

[1950] 2. Receiving and saving feedback

[1951] The server receives feedback via an API endpoint built using Flask. The received feedback is immediately saved to an SQLite database.

[1952] The server sends a "Received" message to the worker's terminal to confirm that the feedback was sent correctly.

[1953] 3. Classification of Opinions

[1954] The server uses Spacy to analyze the received comments using natural language processing and classify them into specific topics or categories. For example, the comment "The project is progressing slowly" would be classified under the category "Project Management."

[1955] The classification results are stored in the database.

[1956] 4. Agenda Generation

[1957] The server extracts key issues and proposals from the categorized opinions and generates an agenda. In this process, the key issues and corresponding proposals are organized for each category.

[1958] The generated agenda is saved in the database.

[1959] 5. Agenda notification

[1960] The server sends the generated agenda to the terminals of workers and administrators, notifying them. Upon notification, the terminals display information about the agenda via pop-up messages or email.

[1961] Specific example

[1962] The worker enters the following comments:

[1963] 1. "The project is progressing slowly."

[1964] 2. "There is a lack of communication."

[1965] 3. "We should introduce new tools."

[1966] 4. "The priorities of the tasks are unclear."

[1967] These opinions are sent to the server's API endpoint and stored in a database. The server uses Spacy to categorize these opinions as follows:

[1968] Category 1: "Project Management"

[1969] Issues: "Slow progress," "Unclear priorities"

[1970] Proposal: "Introduction of new tools"

[1971] Category 2: "Communication"

[1972] Problem: "Lack of communication"

[1973] Next, the server generates an agenda from the classified results and sends an agenda like the following to the terminals of workers and administrators:

[1974] Category 1: "Project Management"

[1975] assignment:

[1976] Slow progress

[1977] Priorities are unclear

[1978] suggestion:

[1979] Introducing new tools

[1980] Category 2: "Communication"

[1981] assignment:

[1982] There is a lack of communication.

[1983] Example of a prompt:

[1984] "The opinion that progress is slow should be categorized under project management."

[1985] "The opinion that there is a lack of communication should be categorized under communication."

[1986] This makes it possible to effectively collect feedback from workers, quickly classify and organize it, and notify managers.

[1987] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1988] (Program processing flow)

[1989] Processing steps

[1990] Step 1:

[1991] Gathering opinions

[1992] Input: Users input their opinions into a smartphone or factory robot interface.

[1993] Operation: The user enters their opinion into a text box on the interface and presses the submit button. The opinion is structured as an HTTP request.

[1994] Output: Opinion data sent to the server.

[1995] Specific example: A worker types and sends the message, "The project is progressing slowly."

[1996] Step 2:

[1997] Receiving and saving feedback

[1998] Input: The server receives an HTTP request.

[1999] Operation: The server uses Flask to receive feedback at the API endpoint and saves the request data to an SQLite database.

[2000] Output: Saved opinion data and a message confirming receipt.

[2001] Specific example: The Flask API saves the feedback "Project progress is slow" to a database and sends a message to the user's terminal confirming receipt.

[2002] Step 3:

[2003] Classification of opinions

[2004] Input: Received opinion data.

[2005] Operation: The server uses Spacy to analyze opinions and classify them into specific topics or categories using natural language processing. This includes text tokenization and entity recognition.

[2006] Output: Classified opinion data.

[2007] Specific example: The opinion that "progress is slow" is categorized under "project management."

[2008] Step 4:

[2009] Agenda generation

[2010] Input: Classified opinion data.

[2011] Operation: The server extracts key issues and proposals based on the classification results and generates an agenda for each category. It organizes the text of the issues and proposals and formats them into an easy-to-read format.

[2012] Output: Agenda data.

[2013] Specific example: An agenda is generated in the "Project Management" category that combines the issue of "slow progress" and the suggestion of "introducing a new tool."

[2014] Step 5:

[2015] Agenda notification

[2016] Input: Generated agenda data.

[2017] Operation: The server sends the agenda to the user's device and sends a notification. Information about the agenda is displayed via a pop-up message or email.

[2018] Output: Agenda notification displayed on the user's terminal.

[2019] Specific example: The administrator's smartphone receives a pop-up message notification for an agenda item in the "Project Management" category.

[2020] (Example of a prompt message)

[2021] "The opinion that progress is slow should be categorized under project management."

[2022] "The opinion that there is a lack of communication should be categorized under communication."

[2023] 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.

[2024] One embodiment of the present invention combines a system that effectively collects, classifies, and organizes user input, and summarizes important issues and proposals as an agenda, with an emotion engine that recognizes the user's emotions. This system provides the following functions:

[2025] Gathering opinions

[2026] The opinions that users submit during brainstorming sessions are entered using a dedicated UI form on their device. Once the user enters their opinion and presses the "Submit" button, the opinion is sent to the server. The device displays a text box and a submit button as the opinion input interface.

[2027] Receiving and saving feedback

[2028] The server has an API endpoint that receives feedback sent from the device. The received feedback is immediately saved to the database. The server sends a "Received" message to the device to confirm that the feedback was sent correctly.

[2029] Classification of Opinions and Sentiment Analysis

[2030] 1. Classification of opinions:

[2031] The server extracts opinions from the database and classifies them using natural language processing (NLP). Generative AI and appropriate algorithms are used to divide the opinions into topics and categories.

[2032] 2. Sentiment analysis:

[2033] The server then uses an emotion engine to analyze the emotions contained in the user's comments. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each comment.

[2034] The analysis results are saved back into the database. Based on this sentiment information, the accuracy of opinion classification improves, enabling classifications that reflect the user's intentions and emotions.

[2035] Agenda generation

[2036] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. In this process, the main issues and corresponding proposals are organized for each category. Furthermore, a flexible agenda is created that takes sentiment information into account.

[2037] The generated agenda is saved in the database and formatted in a user-friendly format.

[2038] Agenda notification

[2039] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[2040] Specific example

[2041] The user enters the following comments:

[2042] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[2043] 2. "Lack of communication" (Emotion: Confusion)

[2044] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[2045] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[2046] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in the database. The server then uses natural language processing to classify these opinions as follows:

[2047] Category 1: "Project Management"

[2048] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[2049] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[2050] Category 2: "Communication"

[2051] Problem: "Lack of communication" (Emotion: Confusion)

[2052] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[2053] Category 1: "Project Management"

[2054] assignment:

[2055] Progress is slow (emotion: dissatisfied)

[2056] Priorities are unclear (emotion: anxiety)

[2057] suggestion:

[2058] Introducing a new tool (emotion: suggestion)

[2059] Category 2: "Communication"

[2060] assignment:

[2061] Lack of communication (Emotion: Confusion)

[2062] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[2063] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[2064] The following describes the processing flow.

[2065] Program processing flow

[2066] Step 1: Enter and submit your comments.

[2067] Terminal:

[2068] Users enter their opinions into a dedicated UI form on their device. The opinion input form has a text box where users enter their opinions. Once they have finished entering their opinions, they confirm them by pressing the "Submit" button.

[2069] Specific actions:

[2070] 1. The user enters their opinion in the text box.

[2071] 2. When the "Send" button is pressed, the device sends the entered comments to the server.

[2072] Step 2: Receiving and saving feedback

[2073] server:

[2074] The server has an API endpoint that receives feedback sent from the device. When feedback reaches the server, it is first saved to the database. Once saving is complete, the server sends a message to the device indicating that processing is finished.

[2075] Specific actions:

[2076] 1. The server receives a request to submit feedback from the terminal.

[2077] 2. Save the received feedback to the database.

[2078] 3. After saving is complete, a "Received" message will be sent to the device.

[2079] Step 3: Classifying opinions and analyzing sentiment

[2080] server:

[2081] The server extracts opinions from the database and classifies them using natural language processing (NLP). It also uses an emotion engine to recognize the emotions contained in the opinions.

[2082] Specific actions:

[2083] 1. The server reads unclassified comments from the database.

[2084] 2. Pass the opinions to a natural language processing module and classify them by topic.

[2085] 3. Pass the categorized opinions to the sentiment engine to generate sentiment data.

[2086] 4. Re-save the emotion data and classification results to the database.

[2087] Step 4: Agenda Generation

[2088] server:

[2089] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. The agenda organizes the issues and proposals for each topic along with sentiment information.

[2090] Specific actions:

[2091] 1. The server retrieves pre-classified opinion and sentiment data from the database.

[2092] 2. Extract the main issues and proposals, as well as related sentiment information, for each category.

[2093] 3. Summarize the issues and proposals to create an agenda.

[2094] 4. Save the agenda to the database.

[2095] Step 5: Agenda Notification

[2096] server:

[2097] The server notifies the user's device of the generated agenda. This notification is sent via methods such as email or a pop-up message.

[2098] Specific actions:

[2099] 1. The server reads the generated agenda from the database.

[2100] 2. Prepare the agenda in a format suitable for notifying users.

[2101] 3. Send the agenda to the user's device and notify them.

[2102] Step 6: User Verification

[2103] User:

[2104] Users check the agenda notified on their devices. Based on the agenda, they decide on the next action plan and how to proceed with the meeting.

[2105] Specific actions:

[2106] 1. The user receives a notification and checks the agenda.

[2107] 2. Based on the agenda, formulate necessary plans and measures.

[2108] Specific example

[2109] The user enters the following comments:

[2110] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[2111] 2. "Lack of communication" (Emotion: Confusion)

[2112] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[2113] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[2114] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[2115] Category 1: "Project Management"

[2116] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[2117] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[2118] Category 2: "Communication"

[2119] Problem: "Lack of communication" (Emotion: Confusion)

[2120] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[2121] Category 1: "Project Management"

[2122] assignment:

[2123] Progress is slow (emotion: dissatisfied)

[2124] Priorities are unclear (emotion: anxiety)

[2125] suggestion:

[2126] Introducing a new tool (emotion: suggestion)

[2127] Category 2: "Communication"

[2128] assignment:

[2129] Lack of communication (Emotion: Confusion)

[2130] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[2131] As described above, the system of the present invention is carried out in a series of steps, starting with user input, followed by saving, classifying, analyzing sentiment, generating an agenda, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[2132] (Example 2)

[2133] 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".

[2134] Traditional opinion gathering systems simply collect user input without adequately understanding their intentions and emotions, making it difficult to generate effective agendas. Furthermore, the lack of emotion-based classification and analysis made it challenging to extract specific issues and proposals that reflected user intent. This resulted in insufficient processing of user feedback, hindering efficient meetings and the development of action plans.

[2135] 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.

[2136] In this invention, the server includes means for collecting user input, means for transmitting the opinions to a central processing unit, means for classifying the opinions using natural language processing, means for analyzing the emotions contained in the opinions, means for extracting issues and proposals from the classified opinions and emotion information and generating an agenda, and means for transmitting and notifying the user of the agenda at their terminal. This enables the generation of a flexible and effective agenda that reflects the user's intentions and emotions.

[2137] A "user" is a person who operates this system and inputs their opinions.

[2138] "Opinions" refer to text data that users input for collection.

[2139] "Means of collection" refers to the interface of a device equipped with a UI form or text box for users to input their opinions.

[2140] "Means of transmission" refers to the function of a terminal that has a communication function for transmitting user input to a central processing unit.

[2141] A "central processing unit" is an information processing system, such as a server or computer, that performs tasks like classifying opinions and analyzing sentiment.

[2142] "Natural language processing" is a technology that enables computers to understand, interpret, and process human language.

[2143] "Means of classification" refer to algorithms and generative AI models that use natural language processing to divide opinions into topics or categories.

[2144] "Means of analyzing emotions" refer to emotion engines and algorithms used to analyze the emotions of users contained in their opinions.

[2145] "Emotional information" refers to emotional data extracted from analyzed opinions.

[2146] A "problem" is an issue extracted from classified opinion and sentiment information.

[2147] A "proposal" is an idea or method extracted as a solution to a problem.

[2148] An "agenda" is a plan for a meeting or action plan generated based on categorized opinions and sentiment information.

[2149] "Means of notification" refers to communication methods such as email or pop-up messages used to convey the generated agenda to the user.

[2150] This invention employs a system that effectively collects, classifies, and organizes user input, summarizing important issues and proposals as an agenda, and combines this with an emotion engine that recognizes the user's emotions. The following hardware and software are used to implement this system.

[2151] Hardware to use:

[2152] 1. Device: A device used to input opinions (such as a personal computer or smartphone)

[2153] 2. Server: A computer system for data processing and storage, classification, and sentiment analysis.

[2154] Software to use:

[2155] 1. UI Form: An interface for users to input their opinions.

[2156] 2. API Endpoint: A means of communication for sending feedback from a device to a server.

[2157] 3. Database: A management system for storing opinions and analysis results (e.g., MySQL, PostgreSQL)

[2158] 4. Natural Language Processing Systems: NLP tools and algorithms for classifying opinions (e.g., BERT, generative AI models)

[2159] 5. Emotion Engine: A tool that analyzes the emotions contained in user feedback (e.g., IBM Watson Natural Language Understanding, Microsoft Text Analytics API)

[2160] 6. Notification System: Means for notifying users of the agenda (e.g., email, WebSockets, push notifications)

[2161] Program processing

[2162] Users utilize a UI form on their device to input their opinions during brainstorming sessions. When a user enters their opinion into a text box and presses the "Submit" button, the opinion is sent to the server's API endpoint via an HTTP POST request.

[2163] The server receives this opinion and immediately saves it to the database. Once saving is complete, a "Received" message is sent to the terminal. The server then extracts the saved opinion from the database and uses natural language processing (NLP) to classify the opinion into topics and categories.

[2164] Next, the server uses an emotion engine to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. Based on this emotion information, the accuracy of opinion classification is improved, enabling classification that reflects the user's intentions and emotions.

[2165] Next, the server extracts key issues and suggestions based on the categorized opinions and sentiment information, and generates an agenda. This agenda organizes the main issues and suggestions for each category and is formatted in a flexible format. The generated agenda is saved in the database and notified to the user. Email and pop-up messages are often used as notification methods.

[2166] Specific example

[2167] Suppose a user enters the following opinion into their device and presses the submit button:

[2168] 1. "The project is progressing slowly." (Emotion: Dissatisfaction)

[2169] 2. "Lack of communication" (Emotion: Confusion)

[2170] 3. "We should introduce a new tool." (Sentiment: Suggestion)

[2171] 4. "The priorities of the tasks are unclear" (Emotion: Anxiety)

[2172] The server receives these opinions and stores them in a database. It then uses a natural language processing system to classify the opinions as follows:

[2173] Category 1: "Project Management"

[2174] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[2175] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[2176] Category 2: "Communication"

[2177] Problem: "Lack of communication" (Emotion: Confusion)

[2178] Next, taking emotional information into consideration, the server generates an agenda based on key issues and suggestions and sends it to the user as follows:

[2179] Category 1: "Project Management"

[2180] assignment:

[2181] Progress is slow (emotion: dissatisfied)

[2182] Priorities are unclear (emotion: anxiety)

[2183] suggestion:

[2184] Introducing a new tool (emotion: suggestion)

[2185] Category 2: "Communication"

[2186] assignment:

[2187] Lack of communication (Emotion: Confusion)

[2188] Example of a prompt

[2189] "Please categorize the following opinions and perform a sentiment analysis: 'Project progress is slow,' 'Lack of communication,' 'New tools should be introduced,' 'Work priorities are unclear.'"

[2190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2191] Step 1:

[2192] The user enters their opinion.

[2193] Users input their opinions during brainstorming sessions using a dedicated UI form on their device. Specifically, they enter their opinions in a text box and press the "Submit" button. For example, they might input the opinion, "The project is progressing slowly." Input: User's opinion (text). Output: User's opinion (text).

[2194] Step 2:

[2195] The device sends the feedback.

[2196] When the user presses the submit button, the device sends the feedback to the server's API endpoint in the form of an HTTP POST request. The submitted data includes the text of the feedback entered by the user. Input: User's feedback (text). Output: Feedback data sent to the server (HTTP request).

[2197] Step 3:

[2198] The server receives and saves the feedback.

[2199] The server receives an HTTP POST request from the terminal, extracts the opinion data, and saves it to the database. MySQL, PostgreSQL, or other databases can be used for storage, depending on data integrity. Once saving is complete, the server sends a "reception complete" message to the terminal. Input: Sent opinion data (HTTP request). Output: Saved opinion data (database), reception complete message (HTTP response).

[2200] Step 4:

[2201] The server categorizes the opinions.

[2202] The server extracts opinion data stored in the database and classifies the opinions using natural language processing (NLP). Specifically, it uses algorithms such as BERT and generative AI models to classify opinions into topics and categories. For example, the opinion "the project is progressing slowly" would be classified under "project management." Input: Opinion data extracted from the database. Output: Classification results (topics and categories).

[2203] Step 5:

[2204] The server analyzes emotions.

[2205] The server uses an emotion engine to analyze the emotions contained in the classified opinion data. For example, it may utilize IBM Watson Natural Language Understanding or the Microsoft Text Analytics API. The emotion engine extracts emotion data (e.g., dissatisfaction, joy, suggestion, etc.) from the opinion text and generates emotion information corresponding to each opinion. Input: Classified opinion data. Output: Emotion information (extracted emotion data).

[2206] Step 6:

[2207] The server generates the agenda.

[2208] Based on classification results and sentiment information, the server extracts key issues and proposals and creates an agenda. For example, it organizes the main issues and corresponding proposals for each category and generates a flexible agenda that reflects sentiment information. Input: Classification results, sentiment information. Output: Generated agenda (list of issues and proposals).

[2209] Step 7:

[2210] The server notifies the user of the agenda.

[2211] The generated agenda is notified to the user's device. Notification methods include email and pop-up messages. SMTP servers, WebSockets, and push notification services can be used. For example, the generated agenda can be sent via email, with the content provided as a URL link. Input: Generated agenda. Output: Notification message sent to the user (email, pop-up notification).

[2212] (Application Example 2)

[2213] 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".

[2214] In factory production lines, there is a lack of efficient means to collect worker feedback and use it to improve work efficiency. Furthermore, because the emotions contained in worker feedback are not considered, the accuracy and effectiveness of improvement suggestions may decrease. To address this challenge, a system is needed that effectively collects and analyzes user feedback and emotions, and generates and notifies appropriate agendas.

[2215] 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 opinions input by the user, means for transmitting the opinions to a central processing device, means for classifying the opinions in the central processing device using natural language processing, means for extracting issues and proposals from the classified opinions and generating an agenda, means for transmitting and notifying the user of the agenda to the user's terminal, means for generating emotion information using an emotion engine that recognizes the emotions contained in the user's opinions, and means for improving the accuracy of agenda generation based on the emotion information. This makes it possible to effectively collect and analyze the opinions and emotions of workers and create and notify them of an agenda that includes specific points for improvement.

[2216] A "user" is an entity that uses the system to input opinions and receives agenda generation and notifications.

[2217] "Opinions" refer to text information entered by users through the system, and include issues and suggestions.

[2218] A "central processing unit" is a device that functions as a server, receiving, classifying, and generating an agenda based on opinions submitted by users.

[2219] "Natural language processing" is a technique used by central processing units to classify opinions into topics and categories, and is a means of analyzing text data.

[2220] An "emotion engine" is a technology or tool that recognizes the emotions contained in a user's opinion and generates emotional information.

[2221] An "agenda" is a compilation of issues and proposals extracted from opinions, presented in a format that is easy for users to understand.

[2222] A "database" is an information storage system for saving received opinions and generated sentiment information and agendas.

[2223] "Notification" refers to the act of informing a user's device of a generated agenda, and includes methods such as email and pop-up messages.

[2224] "Emotional information" refers to emotional data contained in an opinion, generated by an emotion engine.

[2225] One embodiment of the present invention relates to a system for collecting and analyzing the opinions and feelings of workers on a factory production line to generate an efficient agenda. This system is implemented with the following configuration and procedure.

[2226] System Configuration

[2227] 1. Gathering opinions

[2228] Users (workers) input their opinions using smart glasses or tablets. The input opinions are sent to the central processing unit (server) via a dedicated UI form.

[2229] 2. Receiving and saving feedback

[2230] The server receives the feedback sent from the device via the API endpoint and stores it in the database. The server verifies that the feedback has been saved correctly and sends a "Received" message to the device.

[2231] 3. Classification of Opinions and Sentiment Analysis

[2232] The server extracts opinions from the database and uses natural language processing (NLP) to categorize them into topics and categories. It uses a generative AI model to apply an algorithm that automatically categorizes the opinions.

[2233] Next, an emotion engine is used to analyze the emotions contained in the user's opinion. The emotion engine extracts emotion data from the text and generates emotion information corresponding to each opinion. This improves the accuracy of opinion classification and enables classification that reflects the user's intentions and emotions.

[2234] 4. Agenda Generation

[2235] The server extracts key issues and proposals from categorized opinion and sentiment information and generates an agenda. This agenda is organized by category and created in a flexible and effective format.

[2236] 5. Agenda notification

[2237] The generated agenda is sent from the server to the user's device. The notification is sent via methods such as email or a pop-up message, allowing the user to review it and efficiently proceed with their next action plan.

[2238] Program processing

[2239] The server uses the following hardware and software:

[2240] Hardware: Factory robots, smart devices (smart glasses, tablets).

[2241] Software: Python, Node.js, MongoDB, React Native.

[2242] The server classifies opinions using natural language processing (NLP) and utilizes a generative AI model such as OpenAI's GPT-3. Similarly, the emotion engine extracts emotion data using an AI model and categorizes it appropriately.

[2243] Specific example

[2244] The user enters the following comments:

[2245] 1. "The project is progressing slowly."

[2246] 2. "There is a lack of communication."

[2247] 3. "We should introduce new tools."

[2248] 4. "The priorities of the tasks are unclear."

[2249] When a user enters these opinions into their device and presses the submit button, the opinions are sent to the server and stored in a database. The server then uses natural language processing to classify these opinions as follows:

[2250] Category 1: "Project Management"

[2251] Issues: "Slow progress" (emotion: dissatisfaction), "Unclear priorities" (emotion: anxiety)

[2252] Suggestion: "Introduce a new tool" (Emotion: Suggestion)

[2253] Category 2: "Communication"

[2254] Problem: "Lack of communication" (Emotion: Confusion)

[2255] Next, the server generates an agenda from the classified results and sentiment information, and sends the following agenda to the user:

[2256] Category 1: "Project Management"

[2257] assignment:

[2258] Progress is slow (emotion: dissatisfied)

[2259] Priorities are unclear (emotion: anxiety)

[2260] suggestion:

[2261] Introducing a new tool (emotion: suggestion)

[2262] Category 2: "Communication"

[2263] assignment:

[2264] Lack of communication (Emotion: Confusion)

[2265] Based on this agenda, users can efficiently proceed with their next meetings and action plans.

[2266] Example of a prompt

[2267] The following are some examples of prompt statements that can be input to a generative AI model:

[2268] The user submitted the following feedback:

[2269] 1. The project is progressing slowly.

[2270] 2. Lack of communication

[2271] 3. New tools should be introduced.

[2272] 4. The priorities of the tasks are unclear.

[2273] Classify these opinions and feelings, and generate an agenda based on the main issues and suggestions. Feelings can be categorized as "dissatisfaction," "confusion," "suggestions," "anxiety," etc.

[2274] As described above, the system of the present invention is carried out in a series of steps, starting with opinion input, followed by opinion saving, classification, sentiment analysis, agenda generation, notification, and user confirmation. The combination of sentiment engines provides a flexible and effective agenda that reflects the user's intentions and emotions.

[2275] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2276] Step 1:

[2277] Gathering opinions

[2278] Users enter their opinions into a dedicated UI form using smart glasses or a tablet. The opinion input form includes a text box and a submit button. Users enter their opinions into the text box and confirm their opinions by pressing the submit button. At this time, the input is text information, and the output is the text data of the entered opinion.

[2279] Step 2:

[2280] Submit your feedback

[2281] The terminal sends the input opinion text data to the server. The transmission is done using an HTTP POST request to the API endpoint. The input is the opinion text data obtained in step 1, and the output is the opinion data sent to the server.

[2282] Step 3:

[2283] Receiving and saving feedback

[2284] The server receives opinion data sent from the terminal. The server stores the received opinion data in a database. At this time, the input is the sent opinion data, and the output is the opinion data stored in the database. Once saving is complete, the server sends a "reception complete" message to the terminal.

[2285] Step 4:

[2286] Classification of opinions

[2287] The server extracts opinion data from the database and uses natural language processing (NLP) to classify the opinions into topics and categories. A generative AI model is used to automatically classify the opinions based on their content. In this process, the input is the opinion data extracted from the database, and the output is the classified result data.

[2288] Step 5:

[2289] sentiment analysis

[2290] The server performs sentiment analysis on the classified opinion data using an emotion engine. The emotion engine uses an AI model to extract sentiment data from the text and associate sentiment information with each opinion. In this process, the input is the classified opinion data, and the output is the opinion data with sentiment information added.

[2291] Step 6:

[2292] Agenda generation

[2293] The server extracts key issues and proposals based on categorized opinion data and sentiment information, and generates an agenda. In this process, the input is opinion data with added sentiment information, and the output is the generated agenda data. The agenda is organized by category and created in a flexible and effective format.

[2294] Step 7:

[2295] Agenda notification

[2296] The generated agenda is notified from the server to the user's terminal. This notification is sent via email, pop-up message, or other means. In this case, the input is the generated agenda data, and the output is the agenda notification received by the user. The user can then review this and efficiently proceed with their next action plan.

[2297] 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.

[2298] 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.

[2299] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2300] 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.

[2301] 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.

[2302] 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.

[2303] 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.

[2304] 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.

[2305] 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."

[2306] 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.

[2307] 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.

[2308] 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.

[2309] 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.

[2310] 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.

[2311] 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.

[2312] 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.

[2313] 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.

[2314] 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.

[2315] 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.

[2316] 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.

[2317] 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.

[2318] The following is further disclosed regarding the embodiments described above.

[2319] (Claim 1)

[2320] A means of collecting user input,

[2321] Means for transmitting the aforementioned opinion to a central processing unit,

[2322] The central processing unit includes means for classifying the opinions by natural language processing,

[2323] A means of extracting issues and proposals from classified opinions and generating an agenda,

[2324] A means for sending the aforementioned agenda to the user's terminal and notifying them,

[2325] A system that includes this.

[2326] (Claim 2)

[2327] The system according to claim 1, further comprising means for storing opinions in a database as the central processing unit.

[2328] (Claim 3)

[2329] The system according to claim 1, further comprising means for classifying opinions into topics or categories by the aforementioned natural language processing.

[2330] "Example 1"

[2331] (Claim 1)

[2332] A means of collecting user input,

[2333] Means for transmitting the aforementioned opinion to a central processing unit,

[2334] The central processing unit includes means for classifying the opinions by natural language processing,

[2335] A means of extracting issues and proposals from classified opinions and generating an agenda,

[2336] A means for sending the aforementioned agenda to the user's terminal and notifying them,

[2337] A system that includes this.

[2338] (Claim 2)

[2339] The system according to claim 1, further comprising means for receiving the aforementioned opinion and storing it in a database.

[2340] (Claim 3)

[2341] The system according to claim 1, further comprising means for formatting the agenda into a format that is easy for the user to understand.

[2342] "Application Example 1"

[2343] Claims based on a new invention

[2344] (Claim 1)

[2345] A means of collecting user input,

[2346] Means for transmitting the aforementioned opinion to a central processing unit,

[2347] The central processing unit includes means for classifying the opinions by natural language processing,

[2348] A means of extracting issues and proposals from classified opinions and generating an agenda,

[2349] A means for sending the aforementioned agenda to the user's terminal and notifying them,

[2350] A means for workers in an industrial environment to input their opinions into an interface,

[2351] A means of collecting opinions via factory robots and sending them to a server,

[2352] A system that includes this.

[2353] (Claim 2)

[2354] The system according to claim 1, further comprising means for storing opinions in a database as the central processing unit.

[2355] (Claim 3)

[2356] The system according to claim 1, further comprising means for classifying opinions into topics or categories by the aforementioned natural language processing.

[2357] "Example 2 of combining an emotion engine"

[2358] (Claim 1)

[2359] A means of collecting user input,

[2360] Means for transmitting the aforementioned opinion to a central processing unit,

[2361] The central processing unit includes means for classifying the opinions by natural language processing,

[2362] A means for analyzing the emotions contained in the aforementioned opinion,

[2363] A means for extracting issues and proposals from classified opinion and sentiment information and generating an agenda,

[2364] A means for sending the aforementioned agenda to the user's terminal and notifying them,

[2365] A system that includes this.

[2366] (Claim 2)

[2367] The system according to claim 1, further comprising means for storing opinions in a database as the central processing unit.

[2368] (Claim 3)

[2369] The system according to claim 1, further comprising means for classifying opinions into topics or categories by the aforementioned natural language processing.

[2370] "Application example 2 of combining emotional engines"

[2371] (Claim 1)

[2372] A means of collecting user input,

[2373] Means for transmitting the aforementioned opinion to a central processing unit,

[2374] The central processing unit includes means for classifying the opinions by natural language processing,

[2375] A means of extracting issues and proposals from classified opinions and generating an agenda,

[2376] A means for sending the aforementioned agenda to the user's terminal and notifying them,

[2377] A means for generating emotional information using an emotion engine that recognizes the emotions contained in the user's opinion,

[2378] A means to improve the accuracy of agenda generation based on emotional information,

[2379] A system that includes this.

[2380] (Claim 2)

[2381] The system according to claim 1, further comprising means for storing opinions in a database as the central processing unit.

[2382] (Claim 3)

[2383] The system according to claim 1, further comprising means for classifying opinions into topics or categories by the aforementioned natural language processing. [Explanation of symbols]

[2384] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user input, Means for transmitting the aforementioned opinion to a central processing unit, The central processing unit includes means for classifying the opinions using natural language processing, A means of extracting issues and proposals from classified opinions and generating an agenda, A means for sending the aforementioned agenda to the user's terminal and notifying them, A system that includes this.

2. The system according to claim 1, further comprising means for storing opinions in a database as the central processing unit.

3. The system according to claim 1, further comprising means for classifying opinions into topics or categories by the aforementioned natural language processing.

Images