system

Abstract

To provide systems that collect opinions or dissatisfaction from users efficiently, and promptly suggest proper improvement measures.SOLUTION: A system includes: means of receiving opinions or dissatisfaction with respect to machine operations or working environments inputted from each worker working in a factory; means of using an emotion engine to recognize a score indicative of an emotion of the worker from the inputted opinion or dissatisfaction; and means of generating improvement measures relevant to the machine or working environment, using a prompt sentence that instructs to generate improvement measures relevant to the machine or working environment and a generative system AI, on the basis of the inputted opinion or dissatisfaction, and the score of each worker.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Although opinions for business improvement are solicited, there is a problem that no opinions are given. There is also a problem that even if opinions are given, there is no time to implement improvement measures or give advice due to busy business.

Means for Solving the Problems

[0005] As means for solving these problems, a system is proposed that provides means for soliciting opinions for business improvement, means for soliciting dissatisfaction as a countermeasure when no opinions for business improvement are given, and means for soliciting dissatisfaction as a countermeasure when there is no time to give advice on business improvement.

Brief Description of the Drawings

[0006] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when the emotion engine is combined. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when the emotion engine is combined. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Example 2 of Form Example 2 when the emotion engine is combined. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when the emotion engine is combined. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Example 3 of Form Example 3 when the emotion engine is combined. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when the emotion engine is combined.

Embodiments for Carrying out the Invention

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0012] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] One embodiment of the present invention is a system that provides a web-based interface for soliciting opinions related to specific business processes, as a means of collecting opinions for business improvement. This interface allows users to input their opinions and submit them to the system.

[0029] "Example of form 2"

[0030] Furthermore, as a countermeasure in case no suggestions for business improvement are provided, an interface for soliciting complaints related to specific tasks will also be offered. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[0031] "Example of form 3"

[0032] Furthermore, as a measure for situations where there isn't time to provide advice on business process improvement, an interface for soliciting complaints related to specific tasks will also be provided. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The user accesses a web-based interface.

[0036] Step 2: Users enter their opinions related to a specific task.

[0037] Step 3: The user submits their comments to the system.

[0038] "Example of form 2"

[0039] Step 1: The user accesses a web-based interface.

[0040] Step 2: Users enter complaints related to specific tasks.

[0041] Step 3: Send the user's submitted complaint to the system.

[0042] "Example of form 3"

[0043] Step 1: The user accesses a web-based interface.

[0044] Step 2: Users enter complaints related to specific tasks.

[0045] Step 3: Send the user's submitted complaint to the system.

[0046] (Example 1)

[0047] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0048] Conventional business improvement systems struggled to efficiently collect user feedback and complaints and propose appropriate improvement measures. Furthermore, when managers lacked the time to provide business improvement advice, user dissatisfaction accumulated, leading to decreased operational efficiency. Moreover, there was a lack of effective means to manage the collected feedback and complaints and derive appropriate improvement measures.

[0049] 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.

[0050] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing a web-based interface, means for users to input and submit opinions, means for storing the input opinions in a database, means for displaying the stored opinions on a management screen, and means for an administrator to input prompt sentences into a generating AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[0051] "Methods for soliciting opinions on business process improvement" refers to a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business processes.

[0052] "A means of soliciting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that collects complaints and problems on behalf of users when they do not submit suggestions for business improvement.

[0053] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function for collecting complaints and problems from users when administrators do not have time to provide advice on business improvement.

[0054] "Means of providing a web-based interface" refers to a function that provides web pages and applications that users can access via the internet.

[0055] "A means for users to input and submit opinions" refers to a function that allows users to input their own opinions and suggestions and send them to the system.

[0056] "Means for saving submitted opinions to a database" refers to a function for recording and saving opinions and suggestions submitted by users in a database.

[0057] "Means for displaying saved opinions on the administration screen" refers to a function that displays opinions and suggestions saved in the database on the administration screen so that administrators can review them.

[0058] "A means for administrators to input prompts into a generation AI model and obtain suggestions" refers to a function that allows administrators to input specific questions or instructions into a generation AI model and receive suggestions or answers from the AI ​​based on those inputs.

[0059] Modes for carrying out the invention

[0060] This invention is a system for efficiently collecting opinions on business improvement and proposing appropriate improvement measures. A specific embodiment of this system is described below.

[0061] System Configuration

[0062] server

[0063] The server provides a web-based interface using web server software such as Apache® or Nginx. The server has the functionality to receive user feedback and store it in a database. The database uses a database management system such as MySQL® or PostgreSQL.

[0064] terminal

[0065] The terminal is a device that allows users to access a web interface and enter their opinions. The terminal connects to the server using a browser, enters opinions into an opinion input form, and sends the data to the server by pressing the submit button.

[0066] User

[0067] Users utilize the system to provide feedback on business improvements. Users access the web interface, enter their feedback into the feedback form, and submit their opinions by clicking the submit button.

[0068] Program processing

[0069] The server provides a webpage for users to access. This webpage is created using HTML, CSS, and JavaScript (registered trademark). When a user enters their opinion and presses the submit button, the terminal sends the input data to the server as an HTTP POST request.

[0070] The server analyzes the received data and stores it in a database. The stored opinions are displayed on an administration screen accessible to administrators. This administration screen is built using server-side scripting languages ​​such as PHP and Python.

[0071] Administrators review user feedback on the administration screen and, if necessary, input prompts into the AI-generating model. Based on the input prompts, the AI-generating model suggests appropriate improvements and next actions.

[0072] Specific example

[0073] For example, consider a scenario where a user submits a request to "consider introducing a new project management tool." The user accesses the web interface, enters this information into the feedback form, and clicks the submit button. The server receives this data and saves it to the database.

[0074] The administrator reviews this feedback in the administration panel and enters the following prompt for the generating AI model:

[0075] A user has submitted feedback requesting that we consider implementing a new project management tool. Please advise on how to handle this feedback and what improvements should be proposed.

[0076] The generative AI model will suggest appropriate improvements based on this prompt. For example, it might return a suggestion such as, "Introducing a new project management tool could improve the efficiency of task management. Specifically, consider using a task management tool."

[0077] In this way, it becomes possible to efficiently collect feedback from users and quickly propose appropriate improvement measures.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user accesses the web interface.

[0081] Specific action: The user enters a URL into the browser's address bar and presses the Enter key.

[0082] Input: Access request from the user's browser.

[0083] Output: Display of a webpage.

[0084] Data Processing / Calculation: The server uses web server software such as Apache or Nginx to send HTML, CSS, and JavaScript files corresponding to the specified URL to the user's browser.

[0085] Step 2:

[0086] The user enters their opinion into the feedback form and presses the submit button.

[0087] Specific operation: The user enters their opinion using the keyboard and clicks the submit button with the mouse.

[0088] Input: User-submitted opinions.

[0089] Output: Click event of the submit button.

[0090] Data processing / calculation: The browser uses JavaScript to retrieve user input data and sends it to the server as an HTTP POST request.

[0091] Step 3:

[0092] The terminal sends the input data to the server.

[0093] Specific operation: The device sends data using the JavaScript fetch API.

[0094] Input: User-submitted opinion data.

[0095] Output: HTTP POST request to the server.

[0096] Data processing / calculation: The terminal converts the user-entered opinion data into JSON format and includes it in the body of an HTTP POST request, which is then sent to the server.

[0097] Step 4:

[0098] The server saves the data it receives to the database.

[0099] Specific operation: The server uses the Flask framework in Python to receive data and SQLAlchemy to save it to the database.

[0100] Input: Opinion data contained in the body of an HTTP POST request.

[0101] Output: Inserting data into the database.

[0102] Data processing / calculation: The server analyzes the received opinion data and inserts it into the corresponding table in the database.

[0103] Step 5:

[0104] The server displays the opinions saved in the administration panel.

[0105] Specific operation: The server uses an HTML template engine to generate an administration screen and display the saved comments.

[0106] Input: Opinion data stored in the database.

[0107] Output: HTML page of the administration screen.

[0108] Data processing / calculation: The server retrieves opinion data from the database, embeds it in an HTML template, and generates the administration screen.

[0109] Step 6:

[0110] The administrator reviews the feedback on the management screen and, if necessary, enters prompt text into the generated AI model.

[0111] Specific action: The administrator enters a prompt message into the text box on the administration screen and clicks the submit button.

[0112] Input: The prompt text entered by the administrator.

[0113] Output: Request to the generative AI model.

[0114] Data Processing / Calculation: Converts the prompt text entered by the administrator into an appropriate format for sending to the AI ​​model.

[0115] Step 7:

[0116] The generative AI model returns a suggestion.

[0117] Specific operation: The generation AI model generates text based on the input prompt and displays it to the administrator.

[0118] Input: The prompt text entered by the administrator.

[0119] Output: Suggested text from the generative AI model.

[0120] Data Processing / Calculation: The generative AI model analyzes prompt text and generates text that suggests appropriate improvements and next actions.

[0121] (Application Example 1)

[0122] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0123] Traditional systems for soliciting feedback on business improvements had limited means for employees to submit opinions, making it particularly difficult to submit feedback within the factory. Furthermore, the lack of a system for efficiently transmitting feedback to a cloud-based database prevented managers from quickly reviewing the feedback.

[0124] 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.

[0125] In this invention, the server includes means for soliciting suggestions for business improvement, means for soliciting complaints as a countermeasure when no suggestions for business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for being installed on machinery in the factory and providing an interface for employees to input suggestions for business improvement, and means for transmitting the suggestions entered by employees to a cloud-based database. This makes it possible for employees to easily submit suggestions for business improvement within the factory, for those suggestions to be quickly transmitted to a cloud-based database, and for managers to efficiently review them.

[0126] A "means for soliciting opinions on business improvement" refers to a system that provides an interface for employees to submit suggestions for improving the efficiency and performance of their work.

[0127] A "means of collecting complaints" refers to a system that provides an interface for employees to submit complaints or problems regarding their work.

[0128] "Means installed on machinery within a factory to provide an interface for employees to input suggestions for improving operations" refers to a system installed on machinery within a factory that includes input devices such as touchscreens and keyboards that allow employees to directly input their suggestions.

[0129] "A means of sending employee-submitted opinions to a cloud-based database" refers to a system that sends employee-submitted opinions to a cloud-based database via the internet, allowing administrators to review them later.

[0130] A "cloud-based database" is a database stored on a remote server accessible via the internet, allowing multiple users to access, manage, and share data simultaneously.

[0131] A system for carrying out this invention includes means for providing an interface to be installed on machinery in a factory, and means for transmitting employee input to a cloud-based database. Specific embodiments of this system are described below.

[0132] System Configuration

[0133] The system consists of the following main components:

[0134] 1. Interfaces installed on machinery within the factory:

[0135] Hardware: Touchscreen display, keyboard, robots in the factory

[0136] Software: Python, Tkinter library, requests library

[0137] 2. Cloud-based databases:

[0138] Hardware: Remote server

[0139] Software: Database management systems (e.g., MySQL, PostgreSQL)

[0140] Program processing

[0141] The server provides an interface for employees to input feedback for improving work processes. When employees input feedback using a touchscreen display or keyboard, the feedback is retrieved by a Python program. The retrieved feedback is then sent to a cloud-based database as an HTTP POST request using the requests library.

[0142] Data processing and data calculation

[0143] Data Processing: Employee feedback is obtained in text format. The entered text is converted to the appropriate format and prepared as the payload for the HTTP request.

[0144] Data Processing: Opinions submitted to a cloud-based database are stored by the database management system and can be accessed and reviewed by administrators later.

[0145] Specific example

[0146] For example, consider a scenario where a factory employee inputs a suggestion such as "We would like to see new sensors installed to adjust the speed of the production line," and clicks the submit button. In this case, the suggestion entered by the employee is passed to a Python program via a touchscreen display and sent to a cloud-based database using the requests library.

[0147] Example of a prompt

[0148] Please enter your suggestion, "We would like to see new sensors installed to adjust the speed of the production line," and click the submit button.

[0149] In this way, employees can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by managers.

[0150] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0151] Step 1:

[0152] Users input suggestions for business improvement using a touchscreen display or keyboard installed on machinery within the factory. The input suggestions are sent to a terminal in text format. The input data is the text of the user's suggestions, and the output data is the text data stored on the terminal.

[0153] Step 2:

[0154] The terminal receives the input comments and converts them into an appropriate format. Specifically, it prepares the text data as the payload for an HTTP POST request. The input data is the text data stored on the terminal, and the output data is the data prepared as the payload for the HTTP request.

[0155] Step 3:

[0156] The device uses the requests library to send an HTTP POST request to a cloud-based database. The input data is the data prepared as the payload of the HTTP request, and the output data is the opinion data sent to the cloud-based database.

[0157] Step 4:

[0158] The server receives opinion data sent to a cloud-based database and stores it in the database management system. The input data is the opinion data sent to the cloud-based database, and the output data is the opinion data stored in the database.

[0159] Step 5:

[0160] The server provides data through a database management system so that administrators can review the opinion data later. The input data is opinion data stored in the database, and the output data is opinion data provided in a format accessible to administrators.

[0161] In this way, users can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by administrators.

[0162] (Example 2)

[0163] Next, we will describe Example 2 of Form 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".

[0164] Traditional business improvement systems were inadequate in situations where user feedback was lacking or there wasn't enough time to provide business improvement advice. Furthermore, the lack of efficient means to collect and analyze user complaints made it difficult to implement concrete measures for business improvement. This resulted in delays in business efficiency and improvement.

[0165] 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.

[0166] In this invention, the server includes means for the user to input a complaint, means for the user to transmit a complaint, means for the server to receive the complaint, means for the server to store the complaint in a database, and means for the server to analyze the complaint data. This makes it possible to efficiently collect and analyze complaints from users.

[0167] "Means for soliciting opinions on business process improvement" refers to interfaces and functions for collecting opinions from users regarding improvements to business processes.

[0168] "Methods for soliciting complaints as a countermeasure when no suggestions for business improvement are provided" refers to interfaces or functions for collecting complaints from users regarding business operations.

[0169] "Means for users to input complaints" refers to text boxes or input fields where users can enter their complaints in text format.

[0170] "Means for users to submit complaints" refers to a submit button or function that allows users to send their submitted complaints to the system.

[0171] "Means by which a server receives complaints" refers to the functions or processes that a server uses to receive complaints sent by users.

[0172] "Means by which a server stores complaints in a database" refers to the functions or processes used to store received complaints in a database.

[0173] "Means for servers to analyze complaint data" refers to functions and processes for analyzing complaint data stored in a database and extracting trends and frequently occurring keywords.

[0174] This invention is a system for collecting complaints related to specific tasks when no suggestions for business improvement are provided, and then analyzing them. The following describes a specific implementation of this system.

[0175] Hardware and software to be used

[0176] Hardware: Servers, user terminals (PCs, smartphones, etc.)

[0177] Software: Web browsers, web servers (Apache, Nginx, etc.), backend frameworks (Django, Flask, etc.), databases (MySQL, PostgreSQL, etc.)

[0178] System Overview

[0179] This system provides an interface for users to input work-related complaints and send them to a server. The server stores the received complaints in a database and performs periodic analysis.

[0180] User actions

[0181] Users access the system interface using a web browser. The interface displays a text box where users enter work-related complaints. Once they have finished entering their complaints, they click the submit button to send them to the server.

[0182] Specific example

[0183] The user types "The meeting is too long" into the text box and clicks the submit button.

[0184] Server Processing

[0185] The server receives the HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) handles this process. The extracted complaint text is stored in a database. Along with the complaint text, the database also stores metadata such as the date and time of submission and the user ID.

[0186] Data analysis

[0187] The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[0188] Specific example

[0189] The analysis results show that many users complain that "meetings are too long."

[0190] Examples of prompts for generative AI models

[0191] "Analyze the user-submitted complaints about their work and identify the most common types of complaints."

[0192] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[0193] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0194] Step 1:

[0195] Users enter their complaints.

[0196] Specific operation: The user opens a web browser and accesses the system interface. The interface displays a text box where the user enters work-related complaints.

[0197] Input: User-entered complaint text (e.g., "The meeting is too long")

[0198] Output: Complaint text entered in the text box

[0199] Step 2:

[0200] User submits complaint

[0201] Specific operation: The user enters a complaint into a text box and then clicks the submit button. When the submit button is clicked, the entered complaint is sent to the web server as an HTTP POST request.

[0202] Input: Complaint text entered in the text box

[0203] Output: Complaint text sent as an HTTP POST request

[0204] Step 3:

[0205] The server receives complaints.

[0206] Specific operation: The server receives an HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) is responsible for this process.

[0207] Input: Complaint text sent as an HTTP POST request

[0208] Output: Extracted complaint text

[0209] Step 4:

[0210] The server saves complaints to the database.

[0211] Specific operation: The server saves the extracted complaint text to a database. The database also stores metadata such as the date and time of submission and the user ID, along with the complaint text.

[0212] Input: Extracted complaint text, date and time of submission, user ID, and other metadata.

[0213] Output: Complaint text and metadata stored in the database

[0214] Step 5:

[0215] The server analyzes the performance data.

[0216] Specific operation: The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[0217] Input: Complaint data stored in the database

[0218] Output: Trends in dissatisfaction, frequently occurring keywords, and analysis results.

[0219] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[0220] (Application Example 2)

[0221] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0222] Traditional business improvement systems struggled to efficiently collect employee feedback and complaints. In particular, there was a lack of effective means to gather complaints when employees didn't offer suggestions for improvement or when there wasn't enough time to provide advice. Furthermore, within factories, the lack of an easily accessible interface for employees to report complaints meant that sufficient feedback for business improvement wasn't being obtained.

[0223] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing an interface for inputting and transmitting complaints related to specific tasks, and means for transmitting complaints to the server. This provides an environment in which employees can easily report complaints and enables the efficient collection of feedback for business improvement.

[0224] "Methods for soliciting opinions on business improvement" refers to methods or devices for collecting suggestions and opinions from employees regarding the efficiency and improvement of business operations.

[0225] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints about work when suggestions for business improvement cannot be obtained from employees.

[0226] "A means of collecting complaints as a countermeasure when there is no time to provide advice on improving work processes" refers to a method or device for collecting complaints from employees about their work when there is insufficient time to provide advice on improving work processes.

[0227] "Means of providing an interface for inputting and submitting complaints related to specific tasks" refers to a method or device that provides a user interface for employees to input complaints related to specific tasks and submit them to a system.

[0228] "Means for sending complaints to the server" refers to methods or devices for sending complaints entered by employees to a server.

[0229] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints regarding work improvements. Specifically, it constructs a system that allows employees working in a factory to easily report work-related complaints through a robot.

[0230] System program

[0231] This system uses the Python Tkinter library to create a GUI. It will be installed on a factory robot equipped with a touchscreen, providing an interface for employees to input and submit complaints.

[0232] Program Processing Description

[0233] The server processes data using the following hardware and software:

[0234] Hardware: Factory robots equipped with touchscreens

[0235] Software: Python, Tkinter library

[0236] The server retrieves the employee's complaint from the text box and sends it to the server. The specific process is as follows:

[0237] 1. Employees enter their complaints using a touchscreen.

[0238] 2. The entered complaint will be displayed in the text box.

[0239] 3. Clicking the submit button will send your complaint to the server.

[0240] 4. The server saves the received complaints to the database and notifies the administrator.

[0241] Specific example

[0242] If an employee working in the factory finds a new machine too complicated to operate and inefficient, they can use the robot's touchscreen to input their complaint as follows:

[0243] "The new machine is complicated to operate, which makes work inefficient."

[0244] Employees click the submit button, and their complaint is sent to the system. This information reaches administrators, who then consider solutions.

[0245] Example of a prompt

[0246] "Create an application that provides an interface for collecting complaints about work efficiency within the factory. Users should be able to input their complaints and submit them to the system by clicking a submit button."

[0247] In this way, it becomes possible to provide an environment where employees can easily report complaints and to efficiently collect feedback for improving operations.

[0248] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0249] Step 1:

[0250] The user enters their complaint using the touchscreen.

[0251] Input: Text data (content of complaint) entered by the user on the touchscreen.

[0252] Output: The complaints displayed in the text box.

[0253] Specific operation: The user enters their complaint using the keyboard on the touchscreen, and the content is displayed in a text box in real time.

[0254] Step 2:

[0255] The user clicks the submit button.

[0256] Input: The user's action of clicking the submit button.

[0257] Output: An event indicating that the submit button was clicked.

[0258] Specific action: A click event occurs when the user taps the send button on the touchscreen.

[0259] Step 3:

[0260] The device retrieves the complaint details from the text box.

[0261] Input: The complaint entered in the text box.

[0262] Output: Text data of the collected complaints.

[0263] Specific operation: The terminal reads the contents of the text box and stores that text data in a variable.

[0264] Step 4:

[0265] The device sends the complaint to the server.

[0266] Input: Text data of the complaints received.

[0267] Output: Text data of the complaint sent to the server.

[0268] Specific operation: The terminal generates an HTTP request and sends text data containing the complaint to the server.

[0269] Step 5:

[0270] The server saves the dissatisfaction content it received in the database.

[0271] Input: Text data of the dissatisfaction content sent to the server.

[0272] Output: Records of the dissatisfaction content saved in the database.

[0273] Specific operation: The server analyzes the dissatisfaction content it received and saves it as a new record in the database.

[0274] Step 6:

[0275] The server notifies the administrator of the dissatisfaction content.

[0276] Input: Records of the dissatisfaction content saved in the database.

[0277] Output: Notification messages sent to the administrator.

[0278] Specific operation: Based on the dissatisfaction content saved in the database by the server, the server notifies the administrator of the dissatisfaction content via email or the notification system.

[0279] Through the above steps, it is possible to provide an environment where users can easily report dissatisfaction and efficiently collect feedback for business improvement.

[0280] (Example 3)

[0281] Next, Example 3 of Embodiment 3 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0282] Conventional business improvement systems lack the means to address situations where suggestions for business improvement are not provided or where there is insufficient time to offer advice. Furthermore, they struggle to efficiently collect and analyze user complaints and provide concrete advice for business improvement. As a result, the effectiveness of business improvement is limited, and improving user satisfaction is difficult.

[0283] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0284] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for users to input complaints, means for a terminal to send the inputted complaints to the server, means for the server to receive the complaints and store them in a database, means for the server to analyze the stored complaints, and means for the server to send prompt messages to a generating AI model to generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[0285] "Methods for soliciting opinions on business improvement" refers to a function that provides an interface for collecting improvement suggestions related to business operations from users.

[0286] "A means of collecting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that provides an interface for collecting complaints about work processes when users do not submit suggestions for business improvement.

[0287] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from users about their work when there is insufficient time to provide advice on business improvement.

[0288] "A means for users to input complaints" refers to a function that provides an interface for users to input their complaints about their work in text format.

[0289] "Means for sending user-entered complaints to a server" refers to a communication function for sending user-entered complaints to a server via a network.

[0290] "A means for a server to receive complaints and store them in a database" refers to a function that allows a server to receive complaints sent by users and record them in a database.

[0291] "Means for analyzing stored complaints on the server" refers to a function that analyzes complaint data stored in a database and extracts specific patterns or trends.

[0292] "A means by which the server sends prompt messages to the generating AI model and generates business improvement advice" refers to a function in which the server sends prompt messages to the generating AI model based on the analysis results, and the generating AI model generates business improvement advice.

[0293] Modes for carrying out the invention

[0294] This invention is a system that efficiently collects and analyzes opinions and complaints regarding business improvement and provides specific advice for business improvement. A specific embodiment of this system is described below.

[0295] System Configuration

[0296] This system consists of the following main components:

[0297] 1. User Interface: An interface for users to input opinions and complaints related to their work. It operates on a web browser and is built using HTML, CSS, and JavaScript.

[0298] 2. Terminal: A device for sending data entered by the user to the server. Usually, a personal computer or a smartphone is used.

[0299] 3. Server: Receives data sent from the user, stores it in the database, and performs analysis. It uses the Flask framework to process HTTP requests and Python to analyze the data.

[0300] 4. Database: A data storage for saving opinions and dissatisfaction sent from the user. A relational database such as MySQL is used.

[0301] 5. Generated AI Model: An artificial intelligence model for which the server sends a prompt sentence based on the analysis result and generates advice for business improvement.

[0302] Details of Hardware and Software

[0303] User Interface: HTML, CSS, JavaScript

[0304] Terminal: Personal computer, smartphone

[0305] Server: Flask framework, Python

[0306] Database: MySQL

[0307] Generated AI Model: A specific generated AI model (e.g., GPT-3 (registered trademark))

[0308] Specific Example

[0309] Consider the case where the user enters dissatisfaction such as "The meeting time is too long". The user enters "The meeting time is too long" in the text box on the web browser and clicks the send button. By this operation, the user's dissatisfaction is sent from the terminal to the server.

[0310] The server uses the Flask framework to receive HTTP POST requests, parse the received data, and save it to a MySQL database. For example, it executes the SQL query INSERT INTO complaints (text, timestamp) VALUES ('The meeting is too long', '2023-10-01 12:00:00').

[0311] Next, the server uses the Python pandas library to read complaint data from the database and perform analysis. For example, it calculates the frequency of occurrence of specific keywords to understand what kinds of complaints are most common.

[0312] Finally, the server sends a prompt message to the generating AI model based on the analysis results. For example, in response to the complaint "Meetings are too long," the server sends the prompt message "We have received a complaint that meetings are too long. Please generate advice for improving the process in response to this complaint." to the generating AI model. Based on this prompt message, the generating AI model generates advice for improving the process and returns it to the server. The server then provides this advice to the user.

[0313] In this way, it becomes possible to efficiently collect and analyze user complaints and provide specific advice for business improvement. The flow of the specific process in Example 3 will be explained using Figure 15.

[0314] Step 1:

[0315] Users enter their complaints.

[0316] The user accesses the system interface using a web browser. The interface displays a text box for entering work-related complaints and a submit button. The user enters their complaint in the text box and clicks the submit button. The input data is the text of the complaint entered by the user. The output data is the event that the submit button was clicked.

[0317] Step 2:

[0318] The terminal sends the entered complaint to the server.

[0319] When the user clicks the submit button, the device uses JavaScript to send the entered complaint to the server as an HTTP POST request. This request includes the text of the complaint entered by the user. The input data is the text of the complaint entered by the user. The output data is the HTTP POST request sent to the server.

[0320] Step 3:

[0321] The server receives the complaint and saves it to the database.

[0322] The server uses the Flask framework to receive HTTP POST requests. It parses the received complaint text and saves it to a MySQL database. During saving, a new record is added to the database table, recording the complaint text and a timestamp. The input data is the complaint text contained in the HTTP POST request. The output data is the new record saved in the database.

[0323] Step 4:

[0324] The server analyzes the stored complaints.

[0325] The server periodically analyzes complaints stored in the database. The analysis uses the Python pandas library. For example, it calculates the frequency of specific keywords to understand which complaints are most common. The input data is text data of complaints retrieved from the database. The output data is the keyword frequency and complaint trends as a result of the analysis.

[0326] Step 5:

[0327] The server sends prompt messages to the generated AI model, which then generates advice for business improvement.

[0328] The server sends prompt messages to the generating AI model based on the analysis results. These prompt messages include specific details of the user's dissatisfaction. The generating AI model generates business improvement advice based on the received prompt messages and returns it to the server. The server then provides this advice to the user. The input data consists of the analysis results and the generated prompt messages. The output data is the business improvement advice returned by the generating AI model.

[0329] (Application Example 3)

[0330] Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0331] Traditional business improvement systems struggled to efficiently collect and analyze employee feedback and complaints. Furthermore, they lacked effective means of addressing employee dissatisfaction, even when there wasn't time to provide business improvement advice. As a result, feedback for business improvement wasn't fully utilized, hindering improvements in operational efficiency.

[0332] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0333] In this invention, the server includes means for providing an interface for inputting complaints, means for storing the input complaints in a database, means for inputting the stored complaints into a generative AI model and generating analysis results, and means for notifying the administrator of the analysis results. This makes it possible to efficiently collect complaints from employees, analyze them using the generative AI model, provide rapid feedback to administrators, and take concrete actions to improve operations.

[0334] "Means for soliciting opinions on business improvement" refers to methods or devices for collecting opinions from employees regarding improvements to business operations.

[0335] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints from employees when suggestions for business improvement cannot be obtained from them.

[0336] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to provide advice on business improvement.

[0337] "Means of providing an interface for inputting complaints" refers to methods or devices that provide a user interface for employees to input complaints.

[0338] "Means for saving submitted complaints to a database" refers to methods or devices for saving employee-submitted complaints to a database.

[0339] "Means for inputting stored complaints into a generation AI model and generating analysis results" refers to a method or apparatus for inputting complaints stored in a database into a generation AI model and generating analysis results.

[0340] "Means for notifying the administrator of analysis results" refers to methods or devices for notifying the administrator of analysis results generated by the generative AI model.

[0341] The system for implementing this invention is configured as an application installed on robots working in a factory. This system efficiently collects employee complaints, analyzes them using a generative AI model, and notifies managers of the results, thereby promoting business improvement.

[0342] Hardware and software to use

[0343] Hardware: Factory robots, touchscreen displays

[0344] Software: Python, Flask (web framework), SQLite (database), OpenAI® GPT-3 (generative AI model)

[0345] System configuration and operation

[0346] 1. Display of the complaint input interface:

[0347] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. Employees can use this interface to enter their complaints.

[0348] 2. Collection of dissatisfaction data:

[0349] When a user enters their complaint into a text box and clicks the submit button, the server receives the data and saves it to an SQLite database.

[0350] 3. Analysis of dissatisfaction:

[0351] The server periodically retrieves complaint data from the SQLite database and inputs it as prompts into OpenAI GPT-3. The generative AI model generates analysis results based on the input complaints.

[0352] 4. Report to the administrator:

[0353] The server notifies the administrator of the generated analysis results via email. The email is sent using the Python smtplib library.

[0354] Specific example

[0355] For example, a factory employee might type "Machine maintenance is delayed" and click the submit button. This complaint is collected by the server and stored in an SQLite database. The server then inputs this complaint into OpenAI GPT-3 as the following prompt:

[0356] User complaint: Machine maintenance is delayed.

[0357] Prompt to the generating AI model: Generate improvement suggestions for this complaint.

[0358] The AI ​​model generates analysis results such as "The maintenance schedule needs to be reviewed," and the server notifies the administrator of the result via email.

[0359] In this way, by efficiently collecting employee complaints and analyzing them using generative AI models, it becomes possible to provide rapid feedback to managers and take concrete actions to improve operations.

[0360] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0361] Step 1:

[0362] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. The user enters complaints using this interface. The input is the complaint entered by the user in the text box, and the output is the complaint data sent to the server when the submit button is clicked.

[0363] Step 2:

[0364] The server receives the complaint data entered by the user when they click the submit button. The input is the complaint data submitted by the user, and the output is the storage of that complaint data in an SQLite database. Specifically, the server inserts the received complaint data into the database using SQL statements.

[0365] Step 3:

[0366] The server periodically retrieves complaint data from an SQLite database. The complaint data stored in the database serves as input, and the retrieved complaint data is fed into the generating AI model as output. Specifically, the server executes SQL queries to retrieve the complaint data from the database.

[0367] Step 4:

[0368] The server inputs the acquired complaint data into a generative AI model and generates analysis results. The input consists of the acquired complaint data and prompt statements, and the output is the analysis results generated by the generative AI model. Specifically, the server calls the OpenAI GPT-3 API and inputs the complaint data as prompt statements.

[0369] Step 5:

[0370] The server notifies the administrator of the generated analysis results. The input is the analysis results generated by the generative AI model, and the output is a notification email sent to the administrator. Specifically, the server uses the Python smtplib library to send the email containing the analysis results to the administrator.

[0371] In this way, employee complaints can be efficiently collected, analyzed using a generative AI model, and the results can be communicated to managers, enabling concrete actions to be taken to improve operations.

[0372] 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.

[0373] "Example of form 1"

[0374] One embodiment of the present invention incorporates an emotion engine. This emotion engine analyzes emotions from opinions and complaints entered by the user. Specifically, when a user enters an opinion for business improvement, the emotion engine analyzes the emotional elements of that opinion. For example, if a user enters the opinion, "This task takes too long," the emotion engine analyzes the user's feelings of dissatisfaction and stress from that opinion. The results of this analysis are used as reference information when making suggestions for business improvement.

[0375] "Example of form 2"

[0376] Furthermore, the emotion engine also has the function of tracking changes in the user's emotions. Specifically, if a user inputs opinions or complaints multiple times, the emotion engine tracks the changes in the user's emotions from those inputs. For example, it tracks changes in emotions such as a user feeling dissatisfied when they first input an opinion, but feeling satisfied after several inputs. The results of this tracking of emotional changes are used as reference information when updating suggestions for business improvement. "Example Form 3"

[0377] Furthermore, the emotion engine takes user emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement suggestions based on the analysis of user emotions and the tracking of changes in those emotions. For example, for tasks that users are dissatisfied with, it makes suggestions to resolve that dissatisfaction. Conversely, for tasks that users are satisfied with, it makes suggestions to maintain that satisfaction. In this way, the emotion engine enables business improvement suggestions that take user emotions into account.

[0378] The following describes the processing flow for each example of the form.

[0379] "Example of form 1"

[0380] Step 1: Users input their opinions on improving the business process.

[0381] Step 2: The emotion engine analyzes emotional elements from the user's input.

[0382] Step 3: Based on the analysis results, propose business improvements.

[0383] "Example of form 2"

[0384] Step 1: Users input their opinions and complaints multiple times.

[0385] Step 2: The emotion engine tracks changes in emotion based on user input.

[0386] Step 3: Update business improvement proposals based on the results of tracking emotional changes.

[0387] "Example of form 3"

[0388] Step 1: The emotion engine proposes business improvements based on the analysis of the user's emotions and the tracking of changes in those emotions.

[0389] Step 2: For tasks that users are dissatisfied with, propose solutions to address those dissatisfactions.

[0390] Step 3: For tasks that users find satisfying, make suggestions to maintain that satisfaction.

[0391] (Example 1)

[0392] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0393] Conventional business improvement systems lack effective means of collecting and analyzing user feedback and complaints, resulting in inappropriate business improvement proposals. Furthermore, because analysis does not take user emotions into account, it is difficult to create improvement proposals that reflect users' true intentions and feelings.

[0394] 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.

[0395] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for transmitting the received opinions to an emotion analysis engine, means for the emotion analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for making business improvement proposals based on the analysis results. This makes it possible to effectively collect user opinions and complaints and to make business improvement proposals that reflect the user's true intentions and emotions through emotion analysis.

[0396] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for users to input and submit improvement suggestions related to business operations to the system.

[0397] A "means of collecting complaints" refers to a function that provides an interface for users to input and submit their complaints and problems regarding their work to the system.

[0398] "Means for receiving user input" refers to a function that allows a server to receive opinions submitted by users through a web interface.

[0399] "Means for sending received opinions to the sentiment analysis engine" refers to the function by which the server sends the user's opinions received to the sentiment analysis engine.

[0400] "A means by which an emotion analysis engine analyzes opinions and extracts emotional elements" refers to a function in which an emotion analysis engine analyzes a user's opinion and extracts emotional elements from it.

[0401] "Means for saving analysis results" refers to the function of saving the analysis results obtained from the emotion analysis engine to a database.

[0402] "A means of proposing business improvements based on analysis results" refers to a function that generates proposals for business improvements based on saved analysis results and notifies managers and relevant parties.

[0403] Modes for carrying out the invention

[0404] This invention is a system for effectively collecting and analyzing opinions on business improvement. A specific embodiment of this system is described below.

[0405] System Configuration

[0406] This system consists of three main elements: a server, terminals, and users. The server is responsible for soliciting suggestions for business improvement, analyzing the received suggestions, saving the analysis results, and proposing improvements. The terminals provide an interface for users to input their suggestions. Users input their suggestions through the terminals and send them to the system.

[0407] Hardware and software to be used

[0408] The server uses a standard web server and implements a sentiment analysis engine using Python's natural language processing library NLTK and the sentiment analysis library TextBlob. MySQL is used for the database, and Flask is used as the web framework. The terminal uses a web browser (e.g., Google Chrome®) for user access.

[0409] System operation

[0410] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding work improvements into a text box and click the "Submit" button. For example, a user might enter, "This task takes too long."

[0411] The server receives the user's submitted opinion as an HTTP request. The server parses this request and extracts the text data of the opinion. The extracted text data of the opinion is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinion and extract emotions such as "dissatisfaction" and "stress." The analysis results are returned to the server in JSON format.

[0412] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes user opinions and their sentiment analysis results. Based on the saved analysis results, the server makes suggestions for business improvement. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator.

[0413] Examples of specific cases and prompt statements

[0414] As a concrete example, a user accesses a web interface and enters the text, "This task takes too long." The server receives this comment and sends it to the sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" and "stress," and returns the results to the server. The server saves these analysis results to a database and uses them later to suggest improvements to the process.

[0415] Example of a prompt:

[0416] "A user has submitted feedback for business improvement. Please use the sentiment engine to analyze the emotional elements of this feedback and save the results."

[0417] In this way, a system is realized in which servers, terminals, and users each fulfill their respective roles, effectively collecting and analyzing feedback for business improvement.

[0418] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0419] Step 1:

[0420] Users enter their opinions.

[0421] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding business improvements into text boxes and click the "Submit" button. The input data is the text of the opinion entered by the user. The output is the text data of the submitted opinion.

[0422] Step 2:

[0423] The server receives the feedback.

[0424] The server receives the user's submitted opinion as an HTTP POST request. The server uses the Flask framework to parse the request and extract the opinion's text data. The input data is the HTTP POST request, and the output is the extracted opinion's text data.

[0425] Step 3:

[0426] The server sends feedback to the sentiment analysis engine.

[0427] The server sends the extracted opinion text data to the sentiment analysis engine. The sentiment analysis engine is implemented using NLTK, a Python natural language processing library, and TextBlob, a sentiment analysis library. The input data is the opinion text data, and the output is the data sent to the sentiment analysis engine.

[0428] Step 4:

[0429] The emotion analysis engine analyzes the opinion.

[0430] The sentiment analysis engine analyzes the text data of received opinions and extracts emotional elements. For example, it analyzes emotions such as "dissatisfaction" and "stress" from an opinion such as "This task takes too long." The input data is the text data of the opinion, and the output is the sentiment analysis result.

[0431] Step 5:

[0432] The server saves the analysis results.

[0433] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes the user's opinion and its sentiment analysis result. The input data is the sentiment analysis result, and the output is the data saved in the database.

[0434] Step 6:

[0435] The server makes suggestions for business improvements based on the analysis results.

[0436] The server makes suggestions for business improvements based on the saved analysis results. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator. The input data is the analysis results stored in the database, and the output is the generated business improvement suggestions.

[0437] (Application Example 1)

[0438] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0439] Traditional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. Furthermore, they lacked the functionality to analyze the emotional elements of the collected opinions and complaints and provide real-time feedback to managers, making rapid business improvement difficult.

[0440] 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. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of input opinions using an emotion analysis engine, means for notifying the analysis results in real time, and means for saving the analysis results. This makes it possible to efficiently collect opinions and complaints from workers, analyze the emotional elements, and provide feedback to managers in real time.

[0441] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for workers to input suggestions for improving their work processes.

[0442] "A means of soliciting complaints as a countermeasure when no suggestions for improving work processes are made" refers to a function that provides an interface for workers to input complaints about their work.

[0443] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from workers when there is insufficient time to provide advice on business improvement.

[0444] "Means for analyzing the emotional elements of input opinions using an emotion analysis engine" refers to software or algorithms for analyzing the emotional elements of opinions and complaints entered by workers.

[0445] The "means of notifying analysis results in real time" refer to a function that immediately notifies factory managers of the results analyzed by the emotion analysis engine.

[0446] "Means for saving analysis results" refers to a function for saving the results analyzed by the emotion analysis engine to a database or similar.

[0447] One embodiment of this invention is a system that collects opinions and complaints about work improvements from factory workers, analyzes those opinions using an emotion analysis engine, and provides feedback to managers in real time.

[0448] System Configuration

[0449] hardware

[0450] Server: A central computer that collects, analyzes, and stores data.

[0451] Terminal: A smartphone or tablet used by workers to input opinions and complaints.

[0452] Network: The internet or local network used to connect the server and the terminal.

[0453] software

[0454] Web-based interface: A user interface for workers to input opinions and complaints.

[0455] Emotion analysis engine: Software that uses NLTK (Natural Language Toolkit) to analyze the emotional elements of input opinions.

[0456] Database: A database using SQLite to store collected opinions and analysis results.

[0457] Processing flow

[0458] 1. Gathering Feedback: Workers use smartphones or tablets to access a web-based interface and input their opinions and complaints regarding work improvements.

[0459] 2. Data transmission: The submitted opinions and complaints are sent to the server via the network.

[0460] 3. Sentiment Analysis: The sentiment analysis engine on the server analyzes the emotional elements of the input opinions and complaints.

[0461] 4. Real-time notification: Analysis results are notified to factory managers in real time.

[0462] 5. Data Storage: Along with the analysis results, the opinions and complaints entered are stored in the database.

[0463] Specific example

[0464] For example, if a worker inputs feedback such as, "This machine breaks down frequently, causing delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." This analysis result is immediately notified to the factory manager and stored in the database.

[0465] Example of a prompt

[0466] Use an emotion analysis engine to analyze the opinions entered by the workers and extract the emotional elements. For example, from the opinion, "This machine breaks down frequently, which delays the work," extract emotions such as "dissatisfaction" and "stress."

[0467] In this way, it becomes possible to efficiently collect opinions and complaints from workers, analyze emotional elements, and provide feedback to managers in real time. This can lead to rapid improvements in operations.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0469] Step 1:

[0470] Users access a web-based interface using their smartphones or tablets to input feedback and complaints about business improvements. The entered data is saved on the device in text format.

[0471] Step 2:

[0472] The terminal transmits the inputted opinions and complaints to the server via the network. The transmitted data consists of opinions and complaints in text format.

[0473] Step 3:

[0474] The server passes the received opinions and complaints to the sentiment analysis engine. The sentiment analysis engine uses NLTK (Natural Language Toolkit) to analyze the emotional elements of the text data. Specifically, it tokenizes the text data and calculates a sentiment score for each token.

[0475] Step 4:

[0476] The server notifies factory managers in real time of the analysis results obtained from the sentiment analysis engine. The data notified includes the text of opinions and complaints and their sentiment scores. Notifications are made via email and a dedicated administrator dashboard.

[0477] Step 5:

[0478] The server saves the input opinions and complaints, along with the analysis results, to a database. The data saved includes the text of the opinions and complaints, the sentiment score, and a timestamp.

[0479] Step 6:

[0480] Based on the analysis results provided, factory managers will consider specific measures for improving operations. Managers can also access the database to refer to a history of past opinions and complaints.

[0481] In this way, it becomes possible to efficiently collect opinions and complaints from workers, analyze emotional elements, and provide feedback to managers in real time. This can lead to rapid improvements in operations.

[0482] (Example 2)

[0483] Next, we will describe Example 2 of Form 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".

[0484] Traditional business improvement systems lacked the means to effectively collect and analyze user feedback and complaints, resulting in inappropriate business improvement proposals. Furthermore, the absence of features to track changes in user sentiment made it difficult to accurately evaluate the effectiveness of business improvements. Consequently, business improvement proposals often did not align with actual user feelings and needs.

[0485] 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.

[0486] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, sentiment analysis means for analyzing opinions and complaints entered by users, means for tracking changes in the user's emotions, and means for updating business improvement proposals based on changes in emotions. This makes it possible to effectively collect and analyze user opinions and complaints, and furthermore, by tracking changes in the user's emotions, it becomes possible to make more appropriate business improvement proposals.

[0487] "Means for soliciting opinions on business process improvement" refers to interfaces and functions for collecting improvement suggestions related to business processes from users.

[0488] "Methods for collecting complaints" refers to interfaces or functions that allow users to input complaints or problems related to their work and send them to the system.

[0489] "Emotional analysis methods" refer to technologies and algorithms that analyze text data of opinions and complaints entered by users and extract emotions from it.

[0490] "Methods for tracking emotional changes" refer to technologies and algorithms that track changes in a user's emotions based on data of opinions and complaints entered by the user multiple times.

[0491] "Means for updating business improvement proposals" refers to functions and processes for appropriately updating business improvement proposals based on data obtained from emotion analysis methods and methods for tracking changes in emotions.

[0492] This invention is a system for effectively collecting and analyzing opinions and complaints regarding business improvement. A specific embodiment of this system is described below.

[0493] System Configuration

[0494] Hardware and software

[0495] Server: A central processing unit for receiving, storing, and analyzing data. It works in conjunction with a database.

[0496] Device: A device used by users to input opinions and complaints. This includes personal computers, smartphones, and tablets.

[0497] Sentiment analysis engine: Software that analyzes user input data and extracts emotions. A specific example is the use of IBM Watson® Natural Language Understanding.

[0498] Program processing

[0499] Complaint collection interface

[0500] 1. The user enters their complaint:

[0501] The user enters their complaints related to a specific task into a text box on the terminal.

[0502] Example: Type "The meeting is too long."

[0503] 2. Users submit complaints:

[0504] The user clicks the submit button to send their complaint to the server.

[0505] 3. The server receives complaints:

[0506] The server receives complaints sent by users and stores them in a database.

[0507] Emotion tracking using an emotion engine

[0508] 1. The server passes user input to the sentiment engine:

[0509] The server passes the opinions and complaints entered by the user to the emotion engine.

[0510] The emotion engine uses natural language processing techniques to analyze emotions from text.

[0511] 2. The emotion engine analyzes emotions:

[0512] The emotion engine analyzes the text "The meeting is too long" and detects the emotion of "dissatisfaction."

[0513] 3. The server tracks changes in emotions:

[0514] The server passes user input data from multiple instances to the emotion engine to track changes in emotions.

[0515] Example: Track changes in emotion, such as "dissatisfied" on the first input and "satisfied" after several inputs.

[0516] 4. The server reflects emotional changes in suggestions for business improvement:

[0517] The server updates its business improvement suggestions based on emotional change data obtained from the emotion engine.

[0518] Example: Incorporate feedback such as "I felt more satisfied because the meeting time was shortened" into suggestions for improving work processes.

[0519] Specific examples and prompt statements

[0520] Specific example

[0521] User input: "The meeting is too long."

[0522] Emotion engine analysis results: "Dissatisfaction" on the first input, "Satisfaction" after several inputs.

[0523] Business improvement suggestion: "Reducing meeting times will improve user satisfaction."

[0524] Example of a prompt

[0525] "Please enter any complaints related to specific tasks. Example: 'Meetings are too long.'"

[0526] "Please describe any changes in your feelings regarding recent work improvements. For example: 'I'm happy that meeting times have been shortened.'"

[0527] This system collects user complaints and tracks emotional changes using an emotion engine, thereby enabling more effective business improvements.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0529] Step 1:

[0530] Users enter their complaints.

[0531] Input: The user enters their complaints related to a specific task into a text box on the terminal.

[0532] Specific action: The user types "The meeting is too long" into a text box in the browser.

[0533] Output: Text data of the input complaints is generated.

[0534] Step 2:

[0535] User submits complaint

[0536] Input: Text data of complaints entered by the user.

[0537] Specific action: The user clicks the "Send" button.

[0538] Output: The text data of the complaint is sent to the server.

[0539] Step 3:

[0540] The server receives complaints.

[0541] Input: Text data of complaints submitted by the user.

[0542] Specific action: The server receives an HTTP POST request and saves a complaint in the database stating that "the meeting was too long."

[0543] Output: Text data of complaints stored in the database.

[0544] Step 4:

[0545] The server passes user input to the emotion engine.

[0546] Input: Text data of complaints stored in the database.

[0547] Specific operation: The server sends an API request to the sentiment engine and parses the text "The meeting is too long."

[0548] Output: Text data sent to the emotion engine.

[0549] Step 5:

[0550] The emotion engine analyzes emotions.

[0551] Input: Text data sent to the emotion engine.

[0552] Specific operation: The emotion engine uses natural language processing techniques to analyze emotions from text.

[0553] Output: Emotional analysis result: "Dissatisfaction".

[0554] Step 6:

[0555] The server tracks changes in emotions.

[0556] Input: Sentiment analysis results and past input data.

[0557] Specific operation: The server passes past input data to the emotion engine and tracks changes in emotion, from "dissatisfied" on the first input to "satisfied" after several inputs.

[0558] Output: Emotional change data.

[0559] Step 7:

[0560] The server incorporates emotional changes into suggestions for business improvement.

[0561] Input: Data on changes in emotion.

[0562] Specific action: The server incorporates feedback such as "satisfaction increased due to shorter meeting times" into suggestions for business improvement.

[0563] Output: Updated business improvement suggestions.

[0564] (Application Example 2)

[0565] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0566] Conventional business improvement systems have struggled to effectively collect worker opinions and complaints, and to track changes in their emotions. Furthermore, they were unable to generate concrete improvement proposals based on the collected opinions and complaints, resulting in limited effectiveness of business improvement efforts. In particular, they failed to adequately reflect feedback from factory workers, leading to delays in improvements to the work environment and machine operation.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0568] In this invention, the server includes means for receiving opinions or complaints from each worker in the factory regarding the operation of machinery or the work environment; means for recognizing a score indicating the worker's emotions from the input opinions or complaints using an emotion engine; means for generating improvement proposals regarding the machinery or work environment using a prompt message instructing the server to generate improvement proposals regarding the machinery or work environment based on the input opinions or complaints and each worker's score; and a generative AI. The server further includes means for tracking changes in each worker's score, and the means for generating improvement proposals generates improvement proposals regarding the machinery or work environment using a prompt message instructing the server to generate improvement proposals regarding the machinery or work environment based on the input opinions or complaints and each worker's score changes; and the generative AI. This enables effective collection of worker feedback and tracking of emotional changes, thereby generating concrete improvement suggestions and facilitating rapid and effective business improvement. The server may also be installed on machinery within the factory and may further include means for providing an interface for the worker to input the opinion or complaint.

[0569] "Methods for soliciting opinions on business improvement" refer to methods or devices for collecting suggestions and opinions from workers and stakeholders regarding the efficiency and quality improvement of business operations.

[0570] "Methods for collecting complaints" refer to methods or devices for collecting complaints and problems that workers and related parties feel about their work or work environment.

[0571] "Means of providing an interface that allows workers to input dissatisfaction and opinions regarding machine operation and the work environment" refers to methods or devices that provide a user interface for workers to input their opinions and dissatisfaction and send them to a system.

[0572] An "emotion engine" is software or algorithms that analyze input opinions and complaints and recognize a score that indicates the worker's emotions.

[0573] "Means for generating suggestions for improving machine operation and the work environment based on changes in emotions" refers to methods and devices for generating specific improvement suggestions based on changes in emotions analyzed by an emotion engine.

[0574] A system for carrying out this invention includes a server, a terminal used by an operator, and an emotion engine. The server includes means for soliciting suggestions for business improvement, means for soliciting complaints, means for providing an interface in which operators can input complaints and opinions about machine operation and the work environment, an emotion engine, and means for generating suggestions for improving machine operation and the work environment based on changes in emotion.

[0575] Hardware and software to be used

[0576] Hardware: Factory robots, tablets and smartphones used by workers

[0577] Software: Python, TextBlob (sentiment analysis library), JSON (data storage)

[0578] Data processing and data calculation

[0579] The server receives feedback text entered by workers through their terminals. The feedback text is analyzed for sentiment using TextBlob, and a positive or negative score is calculated. This sentiment score is stored in JSON format along with the feedback data. The server tracks changes in a particular worker's sentiment and uses this data to generate specific improvement suggestions.

[0580] Specific example

[0581] For example, if a worker enters feedback stating, "The robot is moving slowly," TextBlob analyzes this text and calculates a negative sentiment score. Then, if the worker enters feedback stating, "The robot's performance has improved recently," a positive sentiment score is calculated. The server tracks these sentiment scores and generates suggestions for improving the robot's performance and work environment based on the changes in sentiment.

[0582] Example of a prompt

[0583] User ID: user123

[0584] Feedback: The robot is moving slowly.

[0585] Emotion score: -0.5

[0586] User ID: user123

[0587] Feedback: The robot's performance has recently improved.

[0588] Emotion score: 0.8

[0589] In this way, feedback systems effectively collect workers' opinions and complaints, track changes in their emotions, generate concrete improvement suggestions, and enable rapid and effective business improvement.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0591] Step 1:

[0592] Users, who are factory workers, use terminals to input feedback expressing opinions or complaints about machine operation or the work environment.

[0593] Input: The user enters feedback in the text box and clicks the submit button.

[0594] Output: Feedback text is sent to the server.

[0595] Specific action: The user types "The robot is moving slowly" and clicks the submit button. Furthermore, it may be installed on machinery within the factory and provide an interface for workers to input opinions or complaints.

[0596] Step 2:

[0597] The server receives the feedback text.

[0598] Input: Feedback text submitted by the user.

[0599] Output: The feedback text is saved to the server.

[0600] Specific action: The server receives the feedback text "The robot is moving slowly" and saves it to the database.

[0601] Step 3:

[0602] The server passes the feedback text to the emotion engine for analysis.

[0603] Input: Saved feedback text.

[0604] Output: Sentiment score (positive or negative score).

[0605] Specific operation: The server uses TextBlob to analyze the text "The robot is slow" and calculates a negative sentiment score (e.g., -0.5).

[0606] Step 4:

[0607] The server saves the emotion score along with the feedback data in JSON format.

[0608] Input: Sentiment score and feedback text.

[0609] Output: Feedback data in JSON format.

[0610] Specific action: The server saves data in JSON format with the following information: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5".

[0611] Step 5:

[0612] The server tracks changes in a specific user's emotions.

[0613] Input: Saved feedback data.

[0614] Output: Data showing changes in the user's emotions.

[0615] Specific operation: The server analyzes "user123's" past feedback data and tracks changes in the sentiment score.

[0616] Step 6:

[0617] The server generates suggestions for improving machine operation and the work environment based on changes in emotions.

[0618] Input: User sentiment change data.

[0619] Output: Improvement suggestions.

[0620] Specific operation: The server generates improvement proposals for the machine or work environment, or generates improvement proposals for the work environment, using a prompt message instructing the server to generate improvement proposals for the machine or work environment based on the input feedback data representing opinions or complaints and the changes in each worker's score, and a generative AI. For example, the server might generate suggestions to improve the robot's operating speed based on the fact that user "user123"'s sentiment score has changed from negative to positive. Example of a prompt: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5, User ID: user123, Feedback: The robot's behavior has improved recently., Sentiment Score: 0.8 Based on this feedback data and the changes in the emotional scores, please generate improvement suggestions for the machinery or work environment within the factory.

[0621] (Example 3)

[0622] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0623] Conventional business improvement systems failed to adequately consider user dissatisfaction and feelings when making suggestions, resulting in limited effectiveness in improving business processes. Furthermore, they lacked sufficient measures to address situations where users did not offer suggestions for improvement or where there was insufficient time to provide advice. This led to unresolved user dissatisfaction and a failure to improve business efficiency.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0625] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, sentiment analysis means for analyzing complaints entered by the user, generation model means for generating business improvement proposals based on the sentiment analysis results, and means for returning the generated proposals to the user. This makes it possible to propose business improvements that take into account the user's emotions, effectively resolve user complaints, and improve business efficiency.

[0626] "Means for soliciting suggestions for business improvement" refers to providing an interface for users to input and submit suggestions for improving business operations to the system.

[0627] "A means of soliciting complaints as a countermeasure when no suggestions for business improvement are provided" refers to a means of providing an interface for users to input complaints about their work and send them to the system.

[0628] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a means of providing an interface for users to input complaints about their work and send them to the system.

[0629] "An emotion analysis method for analyzing user-entered complaints" is a method for analyzing text data entered by a user and determining their emotions.

[0630] A "generative model means for generating business improvement proposals based on sentiment analysis results" is a means for generating specific proposals for business improvement based on the results of sentiment analysis.

[0631] "Means of returning generated suggestions to the user" refers to means of notifying the user of business improvement suggestions generated by the generation model.

[0632] This invention is a system that collects user complaints related to their work, performs sentiment analysis, and generates suggestions for work improvement based on the results. A specific embodiment of this system is described below.

[0633] The server provides an interface for users to input opinions and complaints about their work. This interface is built using HTML and JavaScript, and users access it through a web browser. Users submit their complaints by entering them in a text box and clicking the submit button.

[0634] The terminal sends user-entered complaints to the server using an AJAX request. The server stores the received complaint data in a MySQL database. Specifically, it connects to the database using the Flask framework in Python and executes an INSERT query.

[0635] The server passes the stored dissatisfaction data to the sentiment analysis system. The sentiment analysis system is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The sentiment analysis system analyzes the text data entered by the user and determines the emotion associated with it.

[0636] The sentiment analysis means passes the data to a generative model means that generates business improvement suggestions based on the analysis results. The generative model means generates suggestions using a generative AI model. Specifically, prompt sentences are input to a generative AI model such as GPT-3 to generate suggestions. For example, if the user inputs "Writing daily reports is a hassle," the following prompt sentences are input to the generative AI model:

[0637] A user entered "I find writing daily reports tedious." Please suggest ways to improve the process to address this complaint.

[0638] The generative AI model proposes "the introduction of automated tools for report creation."

[0639] The server returns the generated suggestions to the user. Specifically, it returns the suggestions as a response to the AJAX request, and the terminal displays them to the user. The user can review the suggestions in their browser and take concrete actions to improve their work.

[0640] In this way, the system can propose business improvements that take user emotions into consideration, effectively resolve user dissatisfaction, and improve business efficiency. The flow of a specific process in Example 3 will be explained using Figure 21.

[0641] Step 1:

[0642] Users enter their complaints.

[0643] The user accesses the system interface using a web browser. The interface displays a text box and a submit button. The user enters a complaint about their work in the text box and clicks the submit button. The input data is the text of the complaint entered by the user. The output is the event that the submit button was clicked.

[0644] Step 2:

[0645] The device sends a complaint to the server.

[0646] The terminal sends the user's input complaint to the server using an AJAX request. Specifically, it uses JavaScript to asynchronously send the input text data to the server. The input data is the text of the complaint entered by the user. The output is the complaint data sent to the server.

[0647] Step 3:

[0648] The server saves the complaints to a database.

[0649] The server saves the received complaint data to a MySQL database. Specifically, it uses the Flask framework in Python to connect to the database and execute an INSERT query. The input data is the complaint data sent to the server. The output is the complaint data stored in the database.

[0650] Step 4:

[0651] The server invokes the emotion analysis tool.

[0652] The server passes the stored complaint data to the sentiment analysis tool. The sentiment analysis tool is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The input data is the complaint data stored in the database. The output is the complaint data passed to the sentiment analysis tool.

[0653] Step 5:

[0654] Emotional analysis tools analyze dissatisfaction.

[0655] The sentiment analysis tool analyzes the received dissatisfaction data. Specifically, it uses NLTK and TextBlob to analyze the sentiment of the text. For example, it analyzes the text "Writing daily reports is a hassle" and determines that it is "dissatisfied." The input data is the dissatisfaction data passed to the sentiment analysis tool. The output is the analyzed sentiment data.

[0656] Step 6:

[0657] The emotion analysis tool passes data to a generative model tool that generates suggestions for business improvement.

[0658] The sentiment analysis means passes data to a generative model means that generates business improvement proposals based on the analysis results. The generative model means generates proposals using a generative AI model. Specifically, prompt sentences are input to a generative AI model such as GPT-3, and proposals are generated. The input data is the analyzed sentiment data. The output is the sentiment data passed to the generative model means.

[0659] Step 7:

[0660] The generative model generates suggestions for business improvement.

[0661] The generative model generates specific suggestions for business improvement based on the results of sentiment analysis. For example, if a user inputs "Writing daily reports is a hassle," the following prompt is input to the generative AI model:

[0662] A user entered "I find writing daily reports tedious." Please suggest ways to improve the process to address this complaint.

[0663] The generative AI model proposes "the introduction of an automated tool for report creation." The input data is sentiment data passed to the generative model. The output is the generated business improvement proposal.

[0664] Step 8:

[0665] The server returns a suggestion to the user.

[0666] The server returns the generated suggestions to the user. Specifically, it returns the suggestions as a response to the AJAX request, and the terminal displays them to the user. The input data is the generated business improvement suggestions. The output is the suggestions returned to the user.

[0667] In this way, the system can propose business improvements that take user emotions into consideration, effectively resolve user dissatisfaction, and improve business efficiency.

[0668] (Application Example 3)

[0669] Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0670] Traditional business improvement systems struggled to efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate improvements. Furthermore, they lacked the means to accurately understand and quickly address the feelings of dissatisfied workers. This resulted in problems such as decreased work efficiency and reduced worker motivation.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0672] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for collecting complaints via voice input or text input, means for performing sentiment analysis on the collected complaints, and means for making business improvement proposals based on the results of the sentiment analysis. This makes it possible to efficiently collect complaints and opinions from workers, analyze their emotions, and make appropriate business improvement proposals.

[0673] "Means for soliciting opinions on business improvement" refers to interfaces and methods for collecting suggestions for business improvements from workers and users.

[0674] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to interfaces or methods for collecting complaints from workers or users when suggestions for business improvement cannot be obtained.

[0675] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to an interface or method for collecting complaints from workers or users when there is insufficient time to provide advice on business improvement.

[0676] "Means of collecting complaints via voice or text input" refers to interfaces or methods for workers or users to input complaints via voice or text and send them to the system.

[0677] "Means for emotionally analyzing collected complaints" refers to algorithms and methods for analyzing the content of collected complaints and evaluating the emotions of workers and users.

[0678] "Means for proposing business improvements based on the results of sentiment analysis" refers to algorithms and methods for generating appropriate business improvement proposals based on the results of sentiment analysis and providing them to workers or users.

[0679] In order to implement this invention, the following system configuration is necessary. The system includes means for soliciting opinions on business improvement, means for soliciting complaints, means for collecting complaints via voice input or text input, means for performing sentiment analysis on the collected complaints, and means for making business improvement proposals based on the results of the sentiment analysis.

[0680] Hardware and software to use

[0681] hardware

[0682] Microphone: Used to collect voice input from workers or users.

[0683] Factory robots: They function as an interface for collecting complaints and opinions from workers and proposing improvements to work processes.

[0684] software

[0685] Python: A programming language used to implement programs.

[0686] The speech_recognition library is used to convert speech input into text.

[0687] TextBlob library: Used for sentiment analysis of text.

[0688] The transformers library provides generative AI models for advanced emotion analysis.

[0689] Data processing and data calculation

[0690] Speech recognition

[0691] The server collects the voices of workers and users through microphones and converts the speech to text using the speech_recognition library. This allows the voice input to be incorporated into the system as text data.

[0692] Emotion analysis

[0693] The server inputs the collected text data into the sentiment analysis model of the transformers library and analyzes the emotions. The analysis results are output as one of three emotion labels: positive, negative, or neutral.

[0694] Proposals for business process improvement

[0695] The server generates appropriate suggestions for business improvement based on the results of sentiment analysis. For example, if negative emotions are detected, it makes specific suggestions to alleviate that dissatisfaction. If positive emotions are detected, it makes suggestions to maintain that level of satisfaction.

[0696] Specific example

[0697] When a worker says to the robot, "This machine is difficult to operate," the robot collects the audio and sends it to a server. The server converts the audio to text and performs sentiment analysis. If the analysis result is "negative," it suggests, "To address your dissatisfaction with this task, I propose reviewing the work procedure."

[0698] Example of a prompt

[0699] Worker: This machine is difficult to operate.

[0700] Robot: Recognized text: This machine is difficult to operate.

[0701] Robot: Proposal: To address dissatisfaction with this task, I propose reviewing the work procedure.

[0702] In this way, the system can efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate improvements to operations.

[0703] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0704] Step 1:

[0705] Users input their complaints via voice.

[0706] The user voice-inputs their complaints through a microphone. The input voice data is then transmitted from the microphone to the server.

[0707] Step 2:

[0708] The server converts the audio data into text data.

[0709] The server uses the speech_recognition library to convert received audio data into text data. The input is audio data, and the output is text data.

[0710] Step 3:

[0711] The server performs sentiment analysis on text data.

[0712] The server uses a sentiment analysis model from the transformers library to analyze the sentiment of text data. The input is text data, and the output is sentiment labels (positive, negative, neutral).

[0713] Step 4:

[0714] The server generates suggestions for business improvement based on the results of sentiment analysis.

[0715] The server generates appropriate business improvement suggestions based on the results of sentiment analysis. For example, if a negative emotion is detected, it will provide specific suggestions to address that dissatisfaction. The input is the sentiment label, and the output is the business improvement suggestion.

[0716] Step 5:

[0717] The server provides users with suggestions for improving their work processes.

[0718] The server provides the user with the business improvement suggestions it has generated. Specifically, it displays or reads out the suggestions through the robot's display or voice output. The input is the business improvement suggestions, and the output is feedback to the user.

[0719] 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.

[0720] Data generation model 58 is a form of 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> 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.

[0721] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0722] 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.

[0723] [Second Embodiment]

[0724] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0725] 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.

[0726] The data processing device 12 includes 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.

[0727] Computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. A database 24 and a 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).

[0728] 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.

[0729] 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.

[0730] 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).

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0737] "Example of form 1"

[0738] One embodiment of the present invention is a system that provides a web-based interface for soliciting opinions related to specific business processes, as a means of collecting opinions for business improvement. This interface allows users to input their opinions and submit them to the system.

[0739] "Example of form 2"

[0740] Furthermore, as a countermeasure in case no suggestions for business improvement are provided, an interface for soliciting complaints related to specific tasks will also be offered. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[0741] "Example of form 3"

[0742] Furthermore, as a measure for situations where there isn't time to provide advice on business process improvement, an interface for soliciting complaints related to specific tasks will also be provided. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[0743] The following describes the processing flow for each example of the form.

[0744] "Example of form 1"

[0745] Step 1: The user accesses a web-based interface.

[0746] Step 2: Users enter their opinions related to a specific task.

[0747] Step 3: The user submits their comments to the system.

[0748] "Example of form 2"

[0749] Step 1: The user accesses a web-based interface.

[0750] Step 2: Users enter complaints related to specific tasks.

[0751] Step 3: Send the user's submitted complaint to the system.

[0752] "Example of form 3"

[0753] Step 1: The user accesses a web-based interface.

[0754] Step 2: Users enter complaints related to specific tasks.

[0755] Step 3: Send the user's submitted complaint to the system.

[0756] (Example 1)

[0757] Next, we will describe Example 1 of Form 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".

[0758] Conventional business improvement systems struggled to efficiently collect user feedback and complaints and propose appropriate improvement measures. Furthermore, when managers lacked the time to provide business improvement advice, user dissatisfaction accumulated, leading to decreased operational efficiency. Moreover, there was a lack of effective means to manage the collected feedback and complaints and derive appropriate improvement measures.

[0759] 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.

[0760] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing a web-based interface, means for users to input and submit opinions, means for storing the input opinions in a database, means for displaying the stored opinions on a management screen, and means for an administrator to input prompt sentences into a generating AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[0761] "Methods for soliciting opinions on business process improvement" refers to a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business processes.

[0762] "A means of soliciting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that collects complaints and problems on behalf of users when they do not submit suggestions for business improvement.

[0763] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function for collecting complaints and problems from users when administrators do not have time to provide advice on business improvement.

[0764] "Means of providing a web-based interface" refers to a function that provides web pages and applications that users can access via the internet.

[0765] "A means for users to input and submit opinions" refers to a function that allows users to input their own opinions and suggestions and send them to the system.

[0766] "Means for saving submitted opinions to a database" refers to a function for recording and saving opinions and suggestions submitted by users in a database.

[0767] "Means for displaying saved opinions on the administration screen" refers to a function that displays opinions and suggestions saved in the database on the administration screen so that administrators can review them.

[0768] "A means for administrators to input prompts into a generation AI model and obtain suggestions" refers to a function that allows administrators to input specific questions or instructions into a generation AI model and receive suggestions or answers from the AI ​​based on those inputs.

[0769] Modes for carrying out the invention

[0770] This invention is a system for efficiently collecting opinions on business improvement and proposing appropriate improvement measures. A specific embodiment of this system is described below.

[0771] System Configuration

[0772] server

[0773] The server provides a web-based interface using web server software such as Apache or Nginx. The server has the functionality to receive user feedback and store it in a database. The database uses a database management system such as MySQL or PostgreSQL.

[0774] terminal

[0775] The terminal is a device that allows users to access a web interface and enter their opinions. The terminal connects to the server using a browser, enters opinions into an opinion input form, and sends the data to the server by pressing the submit button.

[0776] User

[0777] Users utilize the system to provide feedback on business improvements. Users access the web interface, enter their feedback into the feedback form, and submit their opinions by clicking the submit button.

[0778] Program processing

[0779] The server provides a webpage for the user to access. This webpage is created using HTML, CSS, and JavaScript. When the user enters their opinion and presses the submit button, the device sends the input data to the server as an HTTP POST request.

[0780] The server analyzes the received data and stores it in a database. The stored opinions are displayed on an administration screen accessible to administrators. This administration screen is built using server-side scripting languages ​​such as PHP and Python.

[0781] Administrators review user feedback on the administration screen and, if necessary, input prompts into the AI-generating model. Based on the input prompts, the AI-generating model suggests appropriate improvements and next actions.

[0782] Specific example

[0783] For example, consider a scenario where a user submits a request to "consider introducing a new project management tool." The user accesses the web interface, enters this information into the feedback form, and clicks the submit button. The server receives this data and saves it to the database.

[0784] The administrator reviews this feedback in the administration panel and enters the following prompt for the generating AI model:

[0785] A user has submitted feedback requesting that we consider implementing a new project management tool. Please advise on how to handle this feedback and what improvements should be proposed.

[0786] The generative AI model will suggest appropriate improvements based on this prompt. For example, it might return a suggestion such as, "Introducing a new project management tool could improve the efficiency of task management. Specifically, consider using a task management tool."

[0787] In this way, it becomes possible to efficiently collect feedback from users and quickly propose appropriate improvement measures.

[0788] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0789] Step 1:

[0790] The user accesses the web interface.

[0791] Specific action: The user enters a URL into the browser's address bar and presses the Enter key.

[0792] Input: Access request from the user's browser.

[0793] Output: Display of a webpage.

[0794] Data Processing / Calculation: The server uses web server software such as Apache or Nginx to send HTML, CSS, and JavaScript files corresponding to the specified URL to the user's browser.

[0795] Step 2:

[0796] The user enters their opinion into the feedback form and presses the submit button.

[0797] Specific operation: The user enters their opinion using the keyboard and clicks the submit button with the mouse.

[0798] Input: User-submitted opinions.

[0799] Output: Click event of the submit button.

[0800] Data processing / calculation: The browser uses JavaScript to retrieve user input data and sends it to the server as an HTTP POST request.

[0801] Step 3:

[0802] The terminal sends the input data to the server.

[0803] Specific operation: The device sends data using the JavaScript fetch API.

[0804] Input: User-submitted opinion data.

[0805] Output: HTTP POST request to the server.

[0806] Data processing / calculation: The terminal converts the user-entered opinion data into JSON format and includes it in the body of an HTTP POST request, which is then sent to the server.

[0807] Step 4:

[0808] The server saves the data it receives to the database.

[0809] Specific operation: The server uses the Flask framework in Python to receive data and SQLAlchemy to save it to the database.

[0810] Input: Opinion data contained in the body of an HTTP POST request.

[0811] Output: Inserting data into the database.

[0812] Data processing / calculation: The server analyzes the received opinion data and inserts it into the corresponding table in the database.

[0813] Step 5:

[0814] The server displays the opinions saved in the administration panel.

[0815] Specific operation: The server uses an HTML template engine to generate an administration screen and display the saved comments.

[0816] Input: Opinion data stored in the database.

[0817] Output: HTML page of the administration screen.

[0818] Data processing / calculation: The server retrieves opinion data from the database, embeds it in an HTML template, and generates the administration screen.

[0819] Step 6:

[0820] The administrator reviews the feedback on the management screen and, if necessary, enters prompt text into the generated AI model.

[0821] Specific action: The administrator enters a prompt message into the text box on the administration screen and clicks the submit button.

[0822] Input: The prompt text entered by the administrator.

[0823] Output: Request to the generative AI model.

[0824] Data Processing / Calculation: Converts the prompt text entered by the administrator into an appropriate format for sending to the AI ​​model.

[0825] Step 7:

[0826] The generative AI model returns a suggestion.

[0827] Specific operation: The generation AI model generates text based on the input prompt and displays it to the administrator.

[0828] Input: The prompt text entered by the administrator.

[0829] Output: Suggested text from the generative AI model.

[0830] Data Processing / Calculation: The generative AI model analyzes prompt text and generates text that suggests appropriate improvements and next actions.

[0831] (Application Example 1)

[0832] Next, we will describe Application Example 1 of Form 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."

[0833] Traditional systems for soliciting feedback on business improvements had limited means for employees to submit opinions, making it particularly difficult to submit feedback within the factory. Furthermore, the lack of a system for efficiently transmitting feedback to a cloud-based database prevented managers from quickly reviewing the feedback.

[0834] 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.

[0835] In this invention, the server includes means for soliciting suggestions for business improvement, means for soliciting complaints as a countermeasure when no suggestions for business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for being installed on machinery in the factory and providing an interface for employees to input suggestions for business improvement, and means for transmitting the suggestions entered by employees to a cloud-based database. This makes it possible for employees to easily submit suggestions for business improvement within the factory, for those suggestions to be quickly transmitted to a cloud-based database, and for managers to efficiently review them.

[0836] A "means for soliciting opinions on business improvement" refers to a system that provides an interface for employees to submit suggestions for improving the efficiency and performance of their work.

[0837] A "means of collecting complaints" refers to a system that provides an interface for employees to submit complaints or problems regarding their work.

[0838] "Means installed on machinery within a factory to provide an interface for employees to input suggestions for improving operations" refers to a system installed on machinery within a factory that includes input devices such as touchscreens and keyboards that allow employees to directly input their suggestions.

[0839] "A means of sending employee-submitted opinions to a cloud-based database" refers to a system that sends employee-submitted opinions to a cloud-based database via the internet, allowing administrators to review them later.

[0840] A "cloud-based database" is a database stored on a remote server accessible via the internet, allowing multiple users to access, manage, and share data simultaneously.

[0841] A system for carrying out this invention includes means for providing an interface to be installed on machinery in a factory, and means for transmitting employee input to a cloud-based database. Specific embodiments of this system are described below.

[0842] System Configuration

[0843] The system consists of the following main components:

[0844] 1. Interfaces installed on machinery within the factory:

[0845] Hardware: Touchscreen display, keyboard, robots in the factory

[0846] Software: Python, Tkinter library, requests library

[0847] 2. Cloud-based databases:

[0848] Hardware: Remote server

[0849] Software: Database management systems (e.g., MySQL, PostgreSQL)

[0850] Program processing

[0851] The server provides an interface for employees to input feedback for improving work processes. When employees input feedback using a touchscreen display or keyboard, the feedback is retrieved by a Python program. The retrieved feedback is then sent to a cloud-based database as an HTTP POST request using the requests library.

[0852] Data processing and data calculation

[0853] Data Processing: Employee feedback is obtained in text format. The entered text is converted to the appropriate format and prepared as the payload for the HTTP request.

[0854] Data Processing: Opinions submitted to a cloud-based database are stored by the database management system and can be accessed and reviewed by administrators later.

[0855] Specific example

[0856] For example, consider a scenario where a factory employee inputs a suggestion such as "We would like to see new sensors installed to adjust the speed of the production line," and clicks the submit button. In this case, the suggestion entered by the employee is passed to a Python program via a touchscreen display and sent to a cloud-based database using the requests library.

[0857] Example of a prompt

[0858] Please enter your suggestion, "We would like to see new sensors installed to adjust the speed of the production line," and click the submit button.

[0859] In this way, employees can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by managers.

[0860] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0861] Step 1:

[0862] Users input suggestions for business improvement using a touchscreen display or keyboard installed on machinery within the factory. The input suggestions are sent to a terminal in text format. The input data is the text of the user's suggestions, and the output data is the text data stored on the terminal.

[0863] Step 2:

[0864] The terminal receives the input comments and converts them into an appropriate format. Specifically, it prepares the text data as the payload for an HTTP POST request. The input data is the text data stored on the terminal, and the output data is the data prepared as the payload for the HTTP request.

[0865] Step 3:

[0866] The device uses the requests library to send an HTTP POST request to a cloud-based database. The input data is the data prepared as the payload of the HTTP request, and the output data is the opinion data sent to the cloud-based database.

[0867] Step 4:

[0868] The server receives opinion data sent to a cloud-based database and stores it in the database management system. The input data is the opinion data sent to the cloud-based database, and the output data is the opinion data stored in the database.

[0869] Step 5:

[0870] The server provides data through a database management system so that administrators can review the opinion data later. The input data is opinion data stored in the database, and the output data is opinion data provided in a format accessible to administrators.

[0871] In this way, users can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by administrators.

[0872] (Example 2)

[0873] Next, we will describe Example 2 of Form 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".

[0874] Traditional business improvement systems were inadequate in situations where user feedback was lacking or there wasn't enough time to provide business improvement advice. Furthermore, the lack of efficient means to collect and analyze user complaints made it difficult to implement concrete measures for business improvement. This resulted in delays in business efficiency and improvement.

[0875] 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.

[0876] In this invention, the server includes means for the user to input a complaint, means for the user to transmit a complaint, means for the server to receive the complaint, means for the server to store the complaint in a database, and means for the server to analyze the complaint data. This makes it possible to efficiently collect and analyze complaints from users.

[0877] "Means for soliciting opinions on business process improvement" refers to interfaces and functions for collecting opinions from users regarding improvements to business processes.

[0878] "Methods for soliciting complaints as a countermeasure when no suggestions for business improvement are provided" refers to interfaces or functions for collecting complaints from users regarding business operations.

[0879] "Means for users to input complaints" refers to text boxes or input fields where users can enter their complaints in text format.

[0880] "Means for users to submit complaints" refers to a submit button or function that allows users to send their submitted complaints to the system.

[0881] "Means by which a server receives complaints" refers to the functions or processes that a server uses to receive complaints sent by users.

[0882] "Means by which a server stores complaints in a database" refers to the functions or processes used to store received complaints in a database.

[0883] "Means for servers to analyze complaint data" refers to functions and processes for analyzing complaint data stored in a database and extracting trends and frequently occurring keywords.

[0884] This invention is a system for collecting complaints related to specific tasks when no suggestions for business improvement are provided, and then analyzing them. The following describes a specific implementation of this system.

[0885] Hardware and software to be used

[0886] Hardware: Servers, user terminals (PCs, smartphones, etc.)

[0887] Software: Web browsers, web servers (Apache, Nginx, etc.), backend frameworks (Django, Flask, etc.), databases (MySQL, PostgreSQL, etc.)

[0888] System Overview

[0889] This system provides an interface for users to input work-related complaints and send them to a server. The server stores the received complaints in a database and performs periodic analysis.

[0890] User actions

[0891] Users access the system interface using a web browser. The interface displays a text box where users enter work-related complaints. Once they have finished entering their complaints, they click the submit button to send them to the server.

[0892] Specific example

[0893] The user types "The meeting is too long" into the text box and clicks the submit button.

[0894] Server Processing

[0895] The server receives the HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) handles this process. The extracted complaint text is stored in a database. Along with the complaint text, the database also stores metadata such as the date and time of submission and the user ID.

[0896] Data analysis

[0897] The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[0898] Specific example

[0899] The analysis results show that many users complain that "meetings are too long."

[0900] Examples of prompts for generative AI models

[0901] "Analyze the user-submitted complaints about their work and identify the most common types of complaints."

[0902] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[0903] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0904] Step 1:

[0905] Users enter their complaints.

[0906] Specific operation: The user opens a web browser and accesses the system interface. The interface displays a text box where the user enters work-related complaints.

[0907] Input: User-entered complaint text (e.g., "The meeting is too long")

[0908] Output: Complaint text entered in the text box

[0909] Step 2:

[0910] User submits complaint

[0911] Specific operation: The user enters a complaint into a text box and then clicks the submit button. When the submit button is clicked, the entered complaint is sent to the web server as an HTTP POST request.

[0912] Input: Complaint text entered in the text box

[0913] Output: Complaint text sent as an HTTP POST request

[0914] Step 3:

[0915] The server receives complaints.

[0916] Specific operation: The server receives an HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) is responsible for this process.

[0917] Input: Complaint text sent as an HTTP POST request

[0918] Output: Extracted complaint text

[0919] Step 4:

[0920] The server saves complaints to the database.

[0921] Specific operation: The server saves the extracted complaint text to a database. The database also stores metadata such as the date and time of submission and the user ID, along with the complaint text.

[0922] Input: Extracted complaint text, date and time of submission, user ID, and other metadata.

[0923] Output: Complaint text and metadata stored in the database

[0924] Step 5:

[0925] The server analyzes the performance data.

[0926] Specific operation: The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[0927] Input: Complaint data stored in the database

[0928] Output: Trends in dissatisfaction, frequently occurring keywords, and analysis results.

[0929] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[0930] (Application Example 2)

[0931] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0932] Traditional business improvement systems struggled to efficiently collect employee feedback and complaints. In particular, there was a lack of effective means to gather complaints when employees didn't offer suggestions for improvement or when there wasn't enough time to provide advice. Furthermore, within factories, the lack of an easily accessible interface for employees to report complaints meant that sufficient feedback for business improvement wasn't being obtained.

[0933] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing an interface for inputting and transmitting complaints related to specific tasks, and means for transmitting complaints to the server. This provides an environment in which employees can easily report complaints and enables the efficient collection of feedback for business improvement.

[0934] "Methods for soliciting opinions on business improvement" refers to methods or devices for collecting suggestions and opinions from employees regarding the efficiency and improvement of business operations.

[0935] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints about work when suggestions for business improvement cannot be obtained from employees.

[0936] "A means of collecting complaints as a countermeasure when there is no time to provide advice on improving work processes" refers to a method or device for collecting complaints from employees about their work when there is insufficient time to provide advice on improving work processes.

[0937] "Means of providing an interface for inputting and submitting complaints related to specific tasks" refers to a method or device that provides a user interface for employees to input complaints related to specific tasks and submit them to a system.

[0938] "Means for sending complaints to the server" refers to methods or devices for sending complaints entered by employees to a server.

[0939] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints regarding work improvements. Specifically, it constructs a system that allows employees working in a factory to easily report work-related complaints through a robot.

[0940] System program

[0941] This system uses the Python Tkinter library to create a GUI. It will be installed on a factory robot equipped with a touchscreen, providing an interface for employees to input and submit complaints.

[0942] Program Processing Description

[0943] The server processes data using the following hardware and software:

[0944] Hardware: Factory robots equipped with touchscreens

[0945] Software: Python, Tkinter library

[0946] The server retrieves the employee's complaint from the text box and sends it to the server. The specific process is as follows:

[0947] 1. Employees enter their complaints using a touchscreen.

[0948] 2. The entered complaint will be displayed in the text box.

[0949] 3. Clicking the submit button will send your complaint to the server.

[0950] 4. The server saves the received complaints to the database and notifies the administrator.

[0951] Specific example

[0952] If an employee working in the factory finds a new machine too complicated to operate and inefficient, they can use the robot's touchscreen to input their complaint as follows:

[0953] "The new machine is complicated to operate, which makes work inefficient."

[0954] Employees click the submit button, and their complaint is sent to the system. This information reaches administrators, who then consider solutions.

[0955] Example of a prompt

[0956] "Create an application that provides an interface for collecting complaints about work efficiency within the factory. Users should be able to input their complaints and submit them to the system by clicking a submit button."

[0957] In this way, it becomes possible to provide an environment where employees can easily report complaints and to efficiently collect feedback for improving operations.

[0958] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0959] Step 1:

[0960] The user enters their complaint using the touchscreen.

[0961] Input: Text data (content of complaint) entered by the user on the touchscreen.

[0962] Output: The complaints displayed in the text box.

[0963] Specific operation: The user enters their complaint using the keyboard on the touchscreen, and the content is displayed in a text box in real time.

[0964] Step 2:

[0965] The user clicks the submit button.

[0966] Input: The user's action of clicking the submit button.

[0967] Output: An event indicating that the submit button was clicked.

[0968] Specific action: A click event occurs when the user taps the send button on the touchscreen.

[0969] Step 3:

[0970] The device retrieves the complaint details from the text box.

[0971] Input: The complaint entered in the text box.

[0972] Output: Text data of the collected complaints.

[0973] Specific operation: The terminal reads the contents of the text box and stores that text data in a variable.

[0974] Step 4:

[0975] The device sends the complaint to the server.

[0976] Input: Text data of the complaints received.

[0977] Output: Text data of the complaint sent to the server.

[0978] Specific operation: The terminal generates an HTTP request and sends text data containing the complaint to the server.

[0979] Step 5:

[0980] The server saves the complaints it receives to a database.

[0981] Input: Text data of the complaint sent to the server.

[0982] Output: Records of complaints stored in the database.

[0983] Specific operation: The server analyzes the complaint received and saves it as a new record in the database.

[0984] Step 6:

[0985] The server notifies the administrator of the complaint.

[0986] Input: Records of complaints stored in the database.

[0987] Output: Notification message sent to the administrator.

[0988] Specific operation: The server notifies the administrator of the complaints based on the complaints stored in the database via email or a notification system.

[0989] By following these steps, it becomes possible to provide an environment where users can easily report complaints and efficiently collect feedback for business improvement.

[0990] (Example 3)

[0991] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0992] Conventional business improvement systems lack the means to address situations where suggestions for business improvement are not provided or where there is insufficient time to offer advice. Furthermore, they struggle to efficiently collect and analyze user complaints and provide concrete advice for business improvement. As a result, the effectiveness of business improvement is limited, and improving user satisfaction is difficult.

[0993] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0994] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for users to input complaints, means for a terminal to send the inputted complaints to the server, means for the server to receive the complaints and store them in a database, means for the server to analyze the stored complaints, and means for the server to send prompt messages to a generating AI model to generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[0995] "Methods for soliciting opinions on business improvement" refers to a function that provides an interface for collecting improvement suggestions related to business operations from users.

[0996] "A means of collecting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that provides an interface for collecting complaints about work processes when users do not submit suggestions for business improvement.

[0997] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from users about their work when there is insufficient time to provide advice on business improvement.

[0998] "A means for users to input complaints" refers to a function that provides an interface for users to input their complaints about their work in text format.

[0999] "Means for sending user-entered complaints to a server" refers to a communication function for sending user-entered complaints to a server via a network.

[1000] "A means for a server to receive complaints and store them in a database" refers to a function that allows a server to receive complaints sent by users and record them in a database.

[1001] "Means for analyzing stored complaints on the server" refers to a function that analyzes complaint data stored in a database and extracts specific patterns or trends.

[1002] "A means by which the server sends prompt messages to the generating AI model and generates business improvement advice" refers to a function in which the server sends prompt messages to the generating AI model based on the analysis results, and the generating AI model generates business improvement advice.

[1003] Modes for carrying out the invention

[1004] This invention is a system that efficiently collects and analyzes opinions and complaints regarding business improvement and provides specific advice for business improvement. A specific embodiment of this system is described below.

[1005] System Configuration

[1006] This system consists of the following main components:

[1007] 1. User Interface: An interface for users to input opinions and complaints related to their work. It operates on a web browser and is built using HTML, CSS, and JavaScript.

[1008] 2. Terminal: A device used to send data entered by the user to the server. Typically, a personal computer or smartphone is used.

[1009] 3. Server: Receives data sent from users, stores it in a database, and performs analysis. Uses the Flask framework to process HTTP requests and Python to analyze the data.

[1010] 4. Database: Data storage for saving opinions and complaints submitted by users. Relational databases such as MySQL are used.

[1011] 5. Generative AI Model: An artificial intelligence model that generates business improvement advice by sending prompt messages based on analysis results from the server.

[1012] Hardware and software details

[1013] User Interface: HTML, CSS, JavaScript

[1014] Devices: PC, smartphone

[1015] Server: Flask framework, Python

[1016] Database: MySQL

[1017] Generative AI model: A specific generative AI model (e.g., GPT-3)

[1018] Specific example

[1019] Consider a scenario where a user submits a complaint that "the meeting is too long." The user types "the meeting is too long" into a text box on their web browser and clicks the submit button. This action sends the user's complaint from their device to the server.

[1020] The server uses the Flask framework to receive HTTP POST requests, parse the received data, and save it to a MySQL database. For example, it executes the SQL query INSERT INTO complaints (text, timestamp) VALUES ('The meeting is too long', '2023-10-01 12:00:00').

[1021] Next, the server uses the Python pandas library to read complaint data from the database and perform analysis. For example, it calculates the frequency of occurrence of specific keywords to understand what kinds of complaints are most common.

[1022] Finally, the server sends a prompt message to the generating AI model based on the analysis results. For example, in response to the complaint "Meetings are too long," the server sends the prompt message "We have received a complaint that meetings are too long. Please generate advice for improving the process in response to this complaint." to the generating AI model. Based on this prompt message, the generating AI model generates advice for improving the process and returns it to the server. The server then provides this advice to the user.

[1023] In this way, it becomes possible to efficiently collect and analyze user complaints and provide specific advice for business improvement. The flow of the specific process in Example 3 will be explained using Figure 15.

[1024] Step 1:

[1025] Users enter their complaints.

[1026] The user accesses the system interface using a web browser. The interface displays a text box for entering work-related complaints and a submit button. The user enters their complaint in the text box and clicks the submit button. The input data is the text of the complaint entered by the user. The output data is the event that the submit button was clicked.

[1027] Step 2:

[1028] The terminal sends the entered complaint to the server.

[1029] When the user clicks the submit button, the device uses JavaScript to send the entered complaint to the server as an HTTP POST request. This request includes the text of the complaint entered by the user. The input data is the text of the complaint entered by the user. The output data is the HTTP POST request sent to the server.

[1030] Step 3:

[1031] The server receives the complaint and saves it to the database.

[1032] The server uses the Flask framework to receive HTTP POST requests. It parses the received complaint text and saves it to a MySQL database. During saving, a new record is added to the database table, recording the complaint text and a timestamp. The input data is the complaint text contained in the HTTP POST request. The output data is the new record saved in the database.

[1033] Step 4:

[1034] The server analyzes the stored complaints.

[1035] The server periodically analyzes complaints stored in the database. The analysis uses the Python pandas library. For example, it calculates the frequency of specific keywords to understand which complaints are most common. The input data is text data of complaints retrieved from the database. The output data is the keyword frequency and complaint trends as a result of the analysis.

[1036] Step 5:

[1037] The server sends prompt messages to the generated AI model, which then generates advice for business improvement.

[1038] The server sends prompt messages to the generating AI model based on the analysis results. These prompt messages include specific details of the user's dissatisfaction. The generating AI model generates business improvement advice based on the received prompt messages and returns it to the server. The server then provides this advice to the user. The input data consists of the analysis results and the generated prompt messages. The output data is the business improvement advice returned by the generating AI model.

[1039] (Application Example 3)

[1040] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1041] Traditional business improvement systems struggled to efficiently collect and analyze employee feedback and complaints. Furthermore, they lacked effective means of addressing employee dissatisfaction, even when there wasn't time to provide business improvement advice. As a result, feedback for business improvement wasn't fully utilized, hindering improvements in operational efficiency.

[1042] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1043] In this invention, the server includes means for providing an interface for inputting complaints, means for storing the input complaints in a database, means for inputting the stored complaints into a generative AI model and generating analysis results, and means for notifying the administrator of the analysis results. This makes it possible to efficiently collect complaints from employees, analyze them using the generative AI model, provide rapid feedback to administrators, and take concrete actions to improve operations.

[1044] "Means for soliciting opinions on business improvement" refers to methods or devices for collecting opinions from employees regarding improvements to business operations.

[1045] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints from employees when suggestions for business improvement cannot be obtained from them.

[1046] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to provide advice on business improvement.

[1047] "Means of providing an interface for inputting complaints" refers to methods or devices that provide a user interface for employees to input complaints.

[1048] "Means for saving submitted complaints to a database" refers to methods or devices for saving employee-submitted complaints to a database.

[1049] "Means for inputting stored complaints into a generation AI model and generating analysis results" refers to a method or apparatus for inputting complaints stored in a database into a generation AI model and generating analysis results.

[1050] "Means for notifying the administrator of analysis results" refers to methods or devices for notifying the administrator of analysis results generated by the generative AI model.

[1051] The system for implementing this invention is configured as an application installed on robots working in a factory. This system efficiently collects employee complaints, analyzes them using a generative AI model, and notifies managers of the results, thereby promoting business improvement.

[1052] Hardware and software to use

[1053] Hardware: Factory robots, touchscreen displays

[1054] Software: Python, Flask (web framework), SQLite (database), OpenAI GPT-3 (generative AI model)

[1055] System configuration and operation

[1056] 1. Display of the complaint input interface:

[1057] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. Employees can use this interface to enter their complaints.

[1058] 2. Collection of dissatisfaction data:

[1059] When a user enters their complaint into a text box and clicks the submit button, the server receives the data and saves it to an SQLite database.

[1060] 3. Analysis of dissatisfaction:

[1061] The server periodically retrieves complaint data from the SQLite database and inputs it as prompts into OpenAI GPT-3. The generative AI model generates analysis results based on the input complaints.

[1062] 4. Report to the administrator:

[1063] The server notifies the administrator of the generated analysis results via email. The email is sent using the Python smtplib library.

[1064] Specific example

[1065] For example, a factory employee might type "Machine maintenance is delayed" and click the submit button. This complaint is collected by the server and stored in an SQLite database. The server then inputs this complaint into OpenAI GPT-3 as the following prompt:

[1066] User complaint: Machine maintenance is delayed.

[1067] Prompt to the generating AI model: Generate improvement suggestions for this complaint.

[1068] The AI ​​model generates analysis results such as "The maintenance schedule needs to be reviewed," and the server notifies the administrator of the result via email.

[1069] In this way, by efficiently collecting employee complaints and analyzing them using generative AI models, it becomes possible to provide rapid feedback to managers and take concrete actions to improve operations.

[1070] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1071] Step 1:

[1072] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. The user enters complaints using this interface. The input is the complaint entered by the user in the text box, and the output is the complaint data sent to the server when the submit button is clicked.

[1073] Step 2:

[1074] The server receives the complaint data entered by the user when they click the submit button. The input is the complaint data submitted by the user, and the output is the storage of that complaint data in an SQLite database. Specifically, the server inserts the received complaint data into the database using SQL statements.

[1075] Step 3:

[1076] The server periodically retrieves complaint data from an SQLite database. The complaint data stored in the database serves as input, and the retrieved complaint data is fed into the generating AI model as output. Specifically, the server executes SQL queries to retrieve the complaint data from the database.

[1077] Step 4:

[1078] The server inputs the acquired complaint data into a generative AI model and generates analysis results. The input consists of the acquired complaint data and prompt statements, and the output is the analysis results generated by the generative AI model. Specifically, the server calls the OpenAI GPT-3 API and inputs the complaint data as prompt statements.

[1079] Step 5:

[1080] The server notifies the administrator of the generated analysis results. The input is the analysis results generated by the generative AI model, and the output is a notification email sent to the administrator. Specifically, the server uses the Python smtplib library to send the email containing the analysis results to the administrator.

[1081] In this way, employee complaints can be efficiently collected, analyzed using a generative AI model, and the results can be communicated to managers, enabling concrete actions to be taken to improve operations.

[1082] 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.

[1083] "Example of form 1"

[1084] One embodiment of the present invention incorporates an emotion engine. This emotion engine analyzes emotions from opinions and complaints entered by the user. Specifically, when a user enters an opinion for business improvement, the emotion engine analyzes the emotional elements of that opinion. For example, if a user enters the opinion, "This task takes too long," the emotion engine analyzes the user's feelings of dissatisfaction and stress from that opinion. The results of this analysis are used as reference information when making suggestions for business improvement.

[1085] "Example of form 2"

[1086] Furthermore, the emotion engine also has the function of tracking changes in the user's emotions. Specifically, if a user enters opinions or complaints multiple times, the emotion engine tracks the changes in the user's emotions from those inputs. For example, it tracks changes in emotions such as a user feeling dissatisfied when they first enter an opinion, but feeling satisfied after several entries. The results of this tracking of emotional changes are used as reference information when updating suggestions for business improvement.

[1087] "Example of form 3"

[1088] Furthermore, the emotion engine takes user emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement suggestions based on the analysis of user emotions and the tracking of changes in those emotions. For example, for tasks that users are dissatisfied with, it makes suggestions to resolve that dissatisfaction. Conversely, for tasks that users are satisfied with, it makes suggestions to maintain that satisfaction. In this way, the emotion engine enables business improvement suggestions that take user emotions into account.

[1089] The following describes the processing flow for each example of the form.

[1090] "Example of form 1"

[1091] Step 1: Users input their opinions on improving the business process.

[1092] Step 2: The emotion engine analyzes emotional elements from the user's input.

[1093] Step 3: Based on the analysis results, propose business improvements.

[1094] "Example of form 2"

[1095] Step 1: Users input their opinions and complaints multiple times.

[1096] Step 2: The emotion engine tracks changes in emotion based on user input.

[1097] Step 3: Update business improvement proposals based on the results of tracking emotional changes.

[1098] "Example of form 3"

[1099] Step 1: The emotion engine proposes business improvements based on the analysis of the user's emotions and the tracking of changes in those emotions.

[1100] Step 2: For tasks that users are dissatisfied with, propose solutions to address those dissatisfactions.

[1101] Step 3: For tasks that users find satisfying, make suggestions to maintain that satisfaction.

[1102] (Example 1)

[1103] Next, we will describe Example 1 of Form 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".

[1104] Conventional business improvement systems lack effective means of collecting and analyzing user feedback and complaints, resulting in inappropriate business improvement proposals. Furthermore, because analysis does not take user emotions into account, it is difficult to create improvement proposals that reflect users' true intentions and feelings.

[1105] 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.

[1106] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for transmitting the received opinions to an emotion analysis engine, means for the emotion analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for making business improvement proposals based on the analysis results. This makes it possible to effectively collect user opinions and complaints and to make business improvement proposals that reflect the user's true intentions and emotions through emotion analysis.

[1107] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for users to input and submit improvement suggestions related to business operations to the system.

[1108] A "means of collecting complaints" refers to a function that provides an interface for users to input and submit their complaints and problems regarding their work to the system.

[1109] "Means for receiving user input" refers to a function that allows a server to receive opinions submitted by users through a web interface.

[1110] "Means for sending received opinions to the sentiment analysis engine" refers to the function by which the server sends the user's opinions received to the sentiment analysis engine.

[1111] "A means by which an emotion analysis engine analyzes opinions and extracts emotional elements" refers to a function in which an emotion analysis engine analyzes a user's opinion and extracts emotional elements from it.

[1112] "Means for saving analysis results" refers to the function of saving the analysis results obtained from the emotion analysis engine to a database.

[1113] "A means of proposing business improvements based on analysis results" refers to a function that generates proposals for business improvements based on saved analysis results and notifies managers and relevant parties.

[1114] Modes for carrying out the invention

[1115] This invention is a system for effectively collecting and analyzing opinions on business improvement. A specific embodiment of this system is described below.

[1116] System Configuration

[1117] This system consists of three main elements: a server, terminals, and users. The server is responsible for soliciting suggestions for business improvement, analyzing the received suggestions, saving the analysis results, and proposing improvements. The terminals provide an interface for users to input their suggestions. Users input their suggestions through the terminals and send them to the system.

[1118] Hardware and software to be used

[1119] The server uses a standard web server and implements a sentiment analysis engine using Python's natural language processing library NLTK and the sentiment analysis library TextBlob. MySQL is used for the database, and Flask is used as the web framework. The terminal uses a web browser (e.g., Google Chrome) for user access.

[1120] System operation

[1121] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding work improvements into a text box and click the "Submit" button. For example, a user might enter, "This task takes too long."

[1122] The server receives the user's submitted opinion as an HTTP request. The server parses this request and extracts the text data of the opinion. The extracted text data of the opinion is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinion and extract emotions such as "dissatisfaction" and "stress." The analysis results are returned to the server in JSON format.

[1123] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes user opinions and their sentiment analysis results. Based on the saved analysis results, the server makes suggestions for business improvement. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator.

[1124] Examples of specific cases and prompt statements

[1125] As a concrete example, a user accesses a web interface and enters the text, "This task takes too long." The server receives this comment and sends it to the sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" and "stress," and returns the results to the server. The server saves these analysis results to a database and uses them later to suggest improvements to the process.

[1126] Example of a prompt:

[1127] "A user has submitted feedback for business improvement. Please use the sentiment engine to analyze the emotional elements of this feedback and save the results."

[1128] In this way, a system is realized in which servers, terminals, and users each fulfill their respective roles, effectively collecting and analyzing feedback for business improvement.

[1129] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1130] Step 1:

[1131] Users enter their opinions.

[1132] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding business improvements into text boxes and click the "Submit" button. The input data is the text of the opinion entered by the user. The output is the text data of the submitted opinion.

[1133] Step 2:

[1134] The server receives the feedback.

[1135] The server receives the user's submitted opinion as an HTTP POST request. The server uses the Flask framework to parse the request and extract the opinion's text data. The input data is the HTTP POST request, and the output is the extracted opinion's text data.

[1136] Step 3:

[1137] The server sends feedback to the sentiment analysis engine.

[1138] The server sends the extracted opinion text data to the sentiment analysis engine. The sentiment analysis engine is implemented using NLTK, a Python natural language processing library, and TextBlob, a sentiment analysis library. The input data is the opinion text data, and the output is the data sent to the sentiment analysis engine.

[1139] Step 4:

[1140] The emotion analysis engine analyzes the opinion.

[1141] The sentiment analysis engine analyzes the text data of received opinions and extracts emotional elements. For example, it analyzes emotions such as "dissatisfaction" and "stress" from an opinion such as "This task takes too long." The input data is the text data of the opinion, and the output is the sentiment analysis result.

[1142] Step 5:

[1143] The server saves the analysis results.

[1144] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes the user's opinion and its sentiment analysis result. The input data is the sentiment analysis result, and the output is the data saved in the database.

[1145] Step 6:

[1146] The server makes suggestions for business improvements based on the analysis results.

[1147] The server makes suggestions for business improvements based on the saved analysis results. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator. The input data is the analysis results stored in the database, and the output is the generated business improvement suggestions.

[1148] (Application Example 1)

[1149] Next, we will describe Application Example 1 of Form 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."

[1150] Traditional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. Furthermore, they lacked the functionality to analyze the emotional elements of the collected opinions and complaints and provide real-time feedback to managers, making rapid business improvement difficult.

[1151] 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. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of input opinions using an emotion analysis engine, means for notifying the analysis results in real time, and means for saving the analysis results. This makes it possible to efficiently collect opinions and complaints from workers, analyze the emotional elements, and provide feedback to managers in real time.

[1152] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for workers to input suggestions for improving their work processes.

[1153] "A means of soliciting complaints as a countermeasure when no suggestions for improving work processes are made" refers to a function that provides an interface for workers to input complaints about their work.

[1154] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from workers when there is insufficient time to provide advice on business improvement.

[1155] "Means for analyzing the emotional elements of input opinions using an emotion analysis engine" refers to software or algorithms for analyzing the emotional elements of opinions and complaints entered by workers.

[1156] The "means of notifying analysis results in real time" refer to a function that immediately notifies factory managers of the results analyzed by the emotion analysis engine.

[1157] "Means for saving analysis results" refers to a function for saving the results analyzed by the emotion analysis engine to a database or similar.

[1158] One embodiment of this invention is a system that collects opinions and complaints about work improvements from factory workers, analyzes those opinions using an emotion analysis engine, and provides feedback to managers in real time.

[1159] System Configuration

[1160] hardware

[1161] Server: A central computer that collects, analyzes, and stores data.

[1162] Terminal: A smartphone or tablet used by workers to input opinions and complaints.

[1163] Network: The internet or local network used to connect the server and the terminal.

[1164] software

[1165] Web-based interface: A user interface for workers to input opinions and complaints.

[1166] Emotion analysis engine: Software that uses NLTK (Natural Language Toolkit) to analyze the emotional elements of input opinions.

[1167] Database: A database using SQLite to store collected opinions and analysis results.

[1168] Processing flow

[1169] 1. Gathering Feedback: Workers use smartphones or tablets to access a web-based interface and input their opinions and complaints regarding work improvements.

[1170] 2. Data transmission: The submitted opinions and complaints are sent to the server via the network.

[1171] 3. Sentiment Analysis: The sentiment analysis engine on the server analyzes the emotional elements of the input opinions and complaints.

[1172] 4. Real-time notification: Analysis results are notified to factory managers in real time.

[1173] 5. Data Storage: Along with the analysis results, the opinions and complaints entered are stored in the database.

[1174] Specific example

[1175] For example, if a worker inputs feedback such as, "This machine breaks down frequently, causing delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." This analysis result is immediately notified to the factory manager and stored in the database.

[1176] Example of a prompt

[1177] Use an emotion analysis engine to analyze the opinions entered by the workers and extract the emotional elements. For example, from the opinion, "This machine breaks down frequently, which delays the work," extract emotions such as "dissatisfaction" and "stress."

[1178] In this way, it becomes possible to efficiently collect opinions and complaints from workers, analyze emotional elements, and provide feedback to managers in real time. This can lead to rapid improvements in operations.

[1179] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1180] Step 1:

[1181] Users access a web-based interface using their smartphones or tablets to input feedback and complaints about business improvements. The entered data is saved on the device in text format.

[1182] Step 2:

[1183] The terminal transmits the inputted opinions and complaints to the server via the network. The transmitted data consists of opinions and complaints in text format.

[1184] Step 3:

[1185] The server passes the received opinions and complaints to the sentiment analysis engine. The sentiment analysis engine uses NLTK (Natural Language Toolkit) to analyze the emotional elements of the text data. Specifically, it tokenizes the text data and calculates a sentiment score for each token.

[1186] Step 4:

[1187] The server notifies factory managers in real time of the analysis results obtained from the sentiment analysis engine. The data notified includes the text of opinions and complaints and their sentiment scores. Notifications are made via email and a dedicated administrator dashboard.

[1188] Step 5:

[1189] The server saves the input opinions and complaints, along with the analysis results, to a database. The data saved includes the text of the opinions and complaints, the sentiment score, and a timestamp.

[1190] Step 6:

[1191] Based on the analysis results provided, factory managers will consider specific measures for improving operations. Managers can also access the database to refer to a history of past opinions and complaints.

[1192] In this way, it becomes possible to efficiently collect opinions and complaints from workers, analyze emotional elements, and provide feedback to managers in real time. This can lead to rapid improvements in operations.

[1193] (Example 2)

[1194] Next, we will describe Example 2 of Form 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".

[1195] Traditional business improvement systems lacked the means to effectively collect and analyze user feedback and complaints, resulting in inappropriate business improvement proposals. Furthermore, the absence of features to track changes in user sentiment made it difficult to accurately evaluate the effectiveness of business improvements. Consequently, business improvement proposals often did not align with actual user feelings and needs.

[1196] 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.

[1197] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, sentiment analysis means for analyzing opinions and complaints entered by users, means for tracking changes in the user's emotions, and means for updating business improvement proposals based on changes in emotions. This makes it possible to effectively collect and analyze user opinions and complaints, and furthermore, by tracking changes in the user's emotions, it becomes possible to make more appropriate business improvement proposals.

[1198] "Means for soliciting opinions on business process improvement" refers to interfaces and functions for collecting improvement suggestions related to business processes from users.

[1199] "Methods for collecting complaints" refers to interfaces or functions that allow users to input complaints or problems related to their work and send them to the system.

[1200] "Emotional analysis methods" refer to technologies and algorithms that analyze text data of opinions and complaints entered by users and extract emotions from it.

[1201] "Methods for tracking emotional changes" refer to technologies and algorithms that track changes in a user's emotions based on data of opinions and complaints entered by the user multiple times.

[1202] "Means for updating business improvement proposals" refers to functions and processes for appropriately updating business improvement proposals based on data obtained from emotion analysis methods and methods for tracking changes in emotions.

[1203] This invention is a system for effectively collecting and analyzing opinions and complaints regarding business improvement. A specific embodiment of this system is described below.

[1204] System Configuration

[1205] Hardware and software

[1206] Server: A central processing unit for receiving, storing, and analyzing data. It works in conjunction with a database.

[1207] Device: A device used by users to input opinions and complaints. This includes personal computers, smartphones, and tablets.

[1208] Sentiment analysis engine: Software that analyzes user input data and extracts emotions. IBM Watson Natural Language Understanding is used as a specific example.

[1209] Program processing

[1210] Complaint collection interface

[1211] 1. The user enters their complaint:

[1212] The user enters their complaints related to a specific task into a text box on the terminal.

[1213] Example: Type "The meeting is too long."

[1214] 2. Users submit complaints:

[1215] The user clicks the submit button to send their complaint to the server.

[1216] 3. The server receives complaints:

[1217] The server receives complaints sent by users and stores them in a database.

[1218] Emotion tracking using an emotion engine

[1219] 1. The server passes user input to the sentiment engine:

[1220] The server passes the opinions and complaints entered by the user to the emotion engine.

[1221] The emotion engine uses natural language processing techniques to analyze emotions from text.

[1222] 2. The emotion engine analyzes emotions:

[1223] The emotion engine analyzes the text "The meeting is too long" and detects the emotion of "dissatisfaction."

[1224] 3. The server tracks changes in emotions:

[1225] The server passes user input data from multiple instances to the emotion engine to track changes in emotions.

[1226] Example: Track changes in emotion, such as "dissatisfied" on the first input and "satisfied" after several inputs.

[1227] 4. The server reflects emotional changes in suggestions for business improvement:

[1228] The server updates its business improvement suggestions based on emotional change data obtained from the emotion engine.

[1229] Example: Incorporate feedback such as "I felt more satisfied because the meeting time was shortened" into suggestions for improving work processes.

[1230] Specific examples and prompt statements

[1231] Specific example

[1232] User input: "The meeting is too long."

[1233] Emotion engine analysis results: "Dissatisfaction" on the first input, "Satisfaction" after several inputs.

[1234] Business improvement suggestion: "Reducing meeting times will improve user satisfaction."

[1235] Example of a prompt

[1236] "Please enter any complaints related to specific tasks. Example: 'Meetings are too long.'"

[1237] "Please describe any changes in your feelings regarding recent work improvements. For example: 'I'm happy that meeting times have been shortened.'"

[1238] This system collects user complaints and tracks emotional changes using an emotion engine, thereby enabling more effective business improvements.

[1239] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1240] Step 1:

[1241] Users enter their complaints.

[1242] Input: The user enters their complaints related to a specific task into a text box on the terminal.

[1243] Specific action: The user types "The meeting is too long" into a text box in the browser.

[1244] Output: Text data of the input complaints is generated.

[1245] Step 2:

[1246] User submits complaint

[1247] Input: Text data of complaints entered by the user.

[1248] Specific action: The user clicks the "Send" button.

[1249] Output: The text data of the complaint is sent to the server.

[1250] Step 3:

[1251] The server receives complaints.

[1252] Input: Text data of complaints submitted by the user.

[1253] Specific action: The server receives an HTTP POST request and saves a complaint in the database stating that "the meeting was too long."

[1254] Output: Text data of complaints stored in the database.

[1255] Step 4:

[1256] The server passes user input to the emotion engine.

[1257] Input: Text data of complaints stored in the database.

[1258] Specific operation: The server sends an API request to the sentiment engine and parses the text "The meeting is too long."

[1259] Output: Text data sent to the emotion engine.

[1260] Step 5:

[1261] The emotion engine analyzes emotions.

[1262] Input: Text data sent to the emotion engine.

[1263] Specific operation: The emotion engine uses natural language processing techniques to analyze emotions from text.

[1264] Output: Emotional analysis result: "Dissatisfaction".

[1265] Step 6:

[1266] The server tracks changes in emotions.

[1267] Input: Sentiment analysis results and past input data.

[1268] Specific operation: The server passes past input data to the emotion engine and tracks changes in emotion, from "dissatisfied" on the first input to "satisfied" after several inputs.

[1269] Output: Emotional change data.

[1270] Step 7:

[1271] The server incorporates emotional changes into suggestions for business improvement.

[1272] Input: Data on changes in emotion.

[1273] Specific action: The server incorporates feedback such as "satisfaction increased due to shorter meeting times" into suggestions for business improvement.

[1274] Output: Updated business improvement suggestions.

[1275] (Application Example 2)

[1276] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1277] Conventional business improvement systems have struggled to effectively collect worker opinions and complaints, and to track changes in their emotions. Furthermore, they were unable to generate concrete improvement proposals based on the collected opinions and complaints, resulting in limited effectiveness of business improvement efforts. In particular, they failed to adequately reflect feedback from factory workers, leading to delays in improvements to the work environment and machine operation.

[1278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1279] In this invention, the server includes means for receiving opinions or complaints from each worker in the factory regarding the operation of machinery or the work environment; means for recognizing a score indicating the worker's emotions from the input opinions or complaints using an emotion engine; means for generating improvement proposals regarding the machinery or work environment using a prompt message instructing the server to generate improvement proposals regarding the machinery or work environment based on the input opinions or complaints and each worker's score; and a generative AI. The server further includes means for tracking changes in each worker's score, and the means for generating improvement proposals generates improvement proposals regarding the machinery or work environment using a prompt message instructing the server to generate improvement proposals regarding the machinery or work environment based on the input opinions or complaints and each worker's score changes; and the generative AI. This enables effective collection of worker feedback and tracking of emotional changes, thereby generating concrete improvement suggestions and facilitating rapid and effective business improvement. The server may also be installed on machinery within the factory and may further include means for providing an interface for the worker to input the opinion or complaint.

[1280] "Methods for soliciting opinions on business improvement" refer to methods or devices for collecting suggestions and opinions from workers and stakeholders regarding the efficiency and quality improvement of business operations.

[1281] "Methods for collecting complaints" refer to methods or devices for collecting complaints and problems that workers and related parties feel about their work or work environment.

[1282] "Means of providing an interface that allows workers to input dissatisfaction and opinions regarding machine operation and the work environment" refers to methods or devices that provide a user interface for workers to input their opinions and dissatisfaction and send them to a system.

[1283] An "emotion engine" is software or algorithms that analyze input opinions and complaints and recognize a score that indicates the worker's emotions.

[1284] "Means for generating suggestions for improving machine operation and the work environment based on changes in emotions" refers to methods and devices for generating specific improvement suggestions based on changes in emotions analyzed by an emotion engine.

[1285] A system for carrying out this invention includes a server, a terminal used by an operator, and an emotion engine. The server includes means for soliciting suggestions for business improvement, means for soliciting complaints, means for providing an interface in which operators can input complaints and opinions about machine operation and the work environment, an emotion engine, and means for generating suggestions for improving machine operation and the work environment based on changes in emotion.

[1286] Hardware and software to be used

[1287] Hardware: Factory robots, tablets and smartphones used by workers

[1288] Software: Python, TextBlob (sentiment analysis library), JSON (data storage)

[1289] Data processing and data calculation

[1290] The server receives feedback text entered by workers through their terminals. The feedback text is analyzed for sentiment using TextBlob, and a positive or negative score is calculated. This sentiment score is stored in JSON format along with the feedback data. The server tracks changes in a particular worker's sentiment and uses this data to generate specific improvement suggestions.

[1291] Specific example

[1292] For example, if a worker enters feedback stating, "The robot is moving slowly," TextBlob analyzes this text and calculates a negative sentiment score. Then, if the worker enters feedback stating, "The robot's performance has improved recently," a positive sentiment score is calculated. The server tracks these sentiment scores and generates suggestions for improving the robot's performance and work environment based on the changes in sentiment.

[1293] Example of a prompt

[1294] User ID: user123

[1295] Feedback: The robot is moving slowly.

[1296] Emotion score: -0.5

[1297] User ID: user123

[1298] Feedback: The robot's performance has recently improved.

[1299] Emotion score: 0.8

[1300] In this way, feedback systems effectively collect workers' opinions and complaints, track changes in their emotions, generate concrete improvement suggestions, and enable rapid and effective business improvement.

[1301] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1302] Step 1:

[1303] Users, who are factory workers, use terminals to input feedback expressing opinions or complaints about machine operation or the work environment.

[1304] Input: The user enters feedback in the text box and clicks the submit button.

[1305] Output: Feedback text is sent to the server.

[1306] Specific action: The user types "The robot is moving slowly" and clicks the submit button. Furthermore, it may be installed on machinery within the factory and provide an interface for workers to input opinions or complaints.

[1307] Step 2:

[1308] The server receives the feedback text.

[1309] Input: Feedback text submitted by the user.

[1310] Output: The feedback text is saved to the server.

[1311] Specific action: The server receives the feedback text "The robot is moving slowly" and saves it to the database.

[1312] Step 3:

[1313] The server passes the feedback text to the emotion engine for analysis.

[1314] Input: Saved feedback text.

[1315] Output: Sentiment score (positive or negative score).

[1316] Specific operation: The server uses TextBlob to analyze the text "The robot is slow" and calculates a negative sentiment score (e.g., -0.5).

[1317] Step 4:

[1318] The server saves the emotion score along with the feedback data in JSON format.

[1319] Input: Sentiment score and feedback text.

[1320] Output: Feedback data in JSON format.

[1321] Specific action: The server saves data in JSON format with the following information: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5".

[1322] Step 5:

[1323] The server tracks changes in a specific user's emotions.

[1324] Input: Saved feedback data.

[1325] Output: Data showing changes in the user's emotions.

[1326] Specific operation: The server analyzes "user123's" past feedback data and tracks changes in the sentiment score.

[1327] Step 6:

[1328] The server generates suggestions for improving machine operation and the work environment based on changes in emotions.

[1329] Input: User sentiment change data.

[1330] Output: Improvement suggestions.

[1331] Specific operation: The server generates improvement proposals for the machine or work environment, or generates improvement proposals for the work environment, using a prompt message instructing the server to generate improvement proposals for the machine or work environment based on the input feedback data representing opinions or complaints and the changes in each worker's score, and a generative AI. For example, the server might generate suggestions to improve the robot's operating speed based on the fact that user "user123"'s sentiment score has changed from negative to positive. Example of a prompt: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5, User ID: user123, Feedback: The robot's behavior has improved recently., Sentiment Score: 0.8 Based on this feedback data and the changes in the emotional scores, please generate improvement suggestions for the machinery or work environment within the factory.

[1332] (Example 3)

[1333] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[1334] Conventional business improvement systems failed to adequately consider user dissatisfaction and feelings when making suggestions, resulting in limited effectiveness in improving business processes. Furthermore, they lacked sufficient measures to address situations where users did not offer suggestions for improvement or where there was insufficient time to provide advice. This led to unresolved user dissatisfaction and a failure to improve business efficiency.

[1335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1336] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, sentiment analysis means for analyzing complaints entered by the user, generation model means for generating business improvement proposals based on the sentiment analysis results, and means for returning the generated proposals to the user. This makes it possible to propose business improvements that take into account the user's emotions, effectively resolve user complaints, and improve business efficiency.

[1337] "Means for soliciting suggestions for business improvement" refers to providing an interface for users to input and submit suggestions for improving business operations to the system.

[1338] "A means of soliciting complaints as a countermeasure when no suggestions for business improvement are provided" refers to a means of providing an interface for users to input complaints about their work and send them to the system.

[1339] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a means of providing an interface for users to input complaints about their work and send them to the system.

[1340] "An emotion analysis method for analyzing user-entered complaints" is a method for analyzing text data entered by a user and determining their emotions.

[1341] A "generative model means for generating business improvement proposals based on sentiment analysis results" is a means for generating specific proposals for business improvement based on the results of sentiment analysis.

[1342] "Means of returning generated suggestions to the user" refers to means of notifying the user of business improvement suggestions generated by the generation model.

[1343] This invention is a system that collects user complaints related to their work, performs sentiment analysis, and generates suggestions for work improvement based on the results. A specific embodiment of this system is described below.

[1344] The server provides an interface for users to input opinions and complaints about their work. This interface is built using HTML and JavaScript, and users access it through a web browser. Users submit their complaints by entering them in a text box and clicking the submit button.

[1345] The terminal sends user-entered complaints to the server using an AJAX request. The server stores the received complaint data in a MySQL database. Specifically, it connects to the database using the Flask framework in Python and executes an INSERT query.

[1346] The server passes the stored dissatisfaction data to the sentiment analysis system. The sentiment analysis system is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The sentiment analysis system analyzes the text data entered by the user and determines the emotion associated with it.

[1347] The sentiment analysis means passes the data to a generative model means that generates business improvement suggestions based on the analysis results. The generative model means generates suggestions using a generative AI model. Specifically, prompt sentences are input to a generative AI model such as GPT-3 to generate suggestions. For example, if the user inputs "Writing daily reports is a hassle," the following prompt sentences are input to the generative AI model:

[1348] A user entered "I find writing daily reports tedious." Please suggest ways to improve the process to address this complaint.

[1349] The generative AI model proposes "the introduction of automated tools for report creation."

[1350] The server returns the generated suggestions to the user. Specifically, it returns the suggestions as a response to the AJAX request, and the terminal displays them to the user. The user can review the suggestions in their browser and take concrete actions to improve their work.

[1351] In this way, the system can propose business improvements that take user emotions into consideration, effectively resolve user dissatisfaction, and improve business efficiency. The flow of a specific process in Example 3 will be explained using Figure 21.

[1352] Step 1:

[1353] Users enter their complaints.

[1354] The user accesses the system interface using a web browser. The interface displays a text box and a submit button. The user enters a complaint about their work in the text box and clicks the submit button. The input data is the text of the complaint entered by the user. The output is the event that the submit button was clicked.

[1355] Step 2:

[1356] The device sends a complaint to the server.

[1357] The terminal sends the user's input complaint to the server using an AJAX request. Specifically, it uses JavaScript to asynchronously send the input text data to the server. The input data is the text of the complaint entered by the user. The output is the complaint data sent to the server.

[1358] Step 3:

[1359] The server saves the complaints to a database.

[1360] The server saves the received complaint data to a MySQL database. Specifically, it uses the Flask framework in Python to connect to the database and execute an INSERT query. The input data is the complaint data sent to the server. The output is the complaint data stored in the database.

[1361] Step 4:

[1362] The server invokes the emotion analysis tool.

[1363] The server passes the stored complaint data to the sentiment analysis tool. The sentiment analysis tool is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The input data is the complaint data stored in the database. The output is the complaint data passed to the sentiment analysis tool.

[1364] Step 5:

[1365] Emotional analysis tools analyze dissatisfaction.

[1366] The sentiment analysis tool analyzes the received dissatisfaction data. Specifically, it uses NLTK and TextBlob to analyze the sentiment of the text. For example, it analyzes the text "Writing daily reports is a hassle" and determines that it is "dissatisfied." The input data is the dissatisfaction data passed to the sentiment analysis tool. The output is the analyzed sentiment data.

[1367] Step 6:

[1368] The emotion analysis tool passes data to a generative model tool that generates suggestions for business improvement.

[1369] The sentiment analysis means passes data to a generative model means that generates business improvement proposals based on the analysis results. The generative model means generates proposals using a generative AI model. Specifically, prompt sentences are input to a generative AI model such as GPT-3, and proposals are generated. The input data is the analyzed sentiment data. The output is the sentiment data passed to the generative model means.

[1370] Step 7:

[1371] The generative model generates suggestions for business improvement.

[1372] The generative model generates specific suggestions for business improvement based on the results of sentiment analysis. For example, if a user inputs "Writing daily reports is a hassle," the following prompt is input to the generative AI model:

[1373] A user entered "I find writing daily reports tedious." Please suggest ways to improve the process to address this complaint.

[1374] The generative AI model proposes "the introduction of an automated tool for report creation." The input data is sentiment data passed to the generative model. The output is the generated business improvement proposal.

[1375] Step 8:

[1376] The server returns a suggestion to the user.

[1377] The server returns the generated suggestions to the user. Specifically, it returns the suggestions as a response to the AJAX request, and the terminal displays them to the user. The input data is the generated business improvement suggestions. The output is the suggestions returned to the user.

[1378] In this way, the system can propose business improvements that take user emotions into consideration, effectively resolve user dissatisfaction, and improve business efficiency.

[1379] (Application Example 3)

[1380] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1381] Traditional business improvement systems struggled to efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate improvements. Furthermore, they lacked the means to accurately understand and quickly address the feelings of dissatisfied workers. This resulted in problems such as decreased work efficiency and reduced worker motivation.

[1382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1383] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for collecting complaints via voice input or text input, means for performing sentiment analysis on the collected complaints, and means for making business improvement proposals based on the results of the sentiment analysis. This makes it possible to efficiently collect complaints and opinions from workers, analyze their emotions, and make appropriate business improvement proposals.

[1384] "Means for soliciting opinions on business improvement" refers to interfaces and methods for collecting suggestions for business improvements from workers and users.

[1385] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to interfaces or methods for collecting complaints from workers or users when suggestions for business improvement cannot be obtained.

[1386] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to an interface or method for collecting complaints from workers or users when there is insufficient time to provide advice on business improvement.

[1387] "Means of collecting complaints via voice or text input" refers to interfaces or methods for workers or users to input complaints via voice or text and send them to the system.

[1388] "Means for emotionally analyzing collected complaints" refers to algorithms and methods for analyzing the content of collected complaints and evaluating the emotions of workers and users.

[1389] "Means for proposing business improvements based on the results of sentiment analysis" refers to algorithms and methods for generating appropriate business improvement proposals based on the results of sentiment analysis and providing them to workers or users.

[1390] In order to implement this invention, the following system configuration is necessary. The system includes means for soliciting opinions on business improvement, means for soliciting complaints, means for collecting complaints via voice input or text input, means for performing sentiment analysis on the collected complaints, and means for making business improvement proposals based on the results of the sentiment analysis.

[1391] Hardware and software to use

[1392] hardware

[1393] Microphone: Used to collect voice input from workers or users.

[1394] Factory robots: They function as an interface for collecting complaints and opinions from workers and proposing improvements to work processes.

[1395] software

[1396] Python: A programming language used to implement programs.

[1397] The speech_recognition library is used to convert speech input into text.

[1398] TextBlob library: Used for sentiment analysis of text.

[1399] The transformers library provides generative AI models for advanced emotion analysis.

[1400] Data processing and data calculation

[1401] Speech recognition

[1402] The server collects the voices of workers and users through microphones and converts the speech to text using the speech_recognition library. This allows the voice input to be incorporated into the system as text data.

[1403] Emotion analysis

[1404] The server inputs the collected text data into the sentiment analysis model of the transformers library and analyzes the emotions. The analysis results are output as one of three emotion labels: positive, negative, or neutral.

[1405] Proposals for business process improvement

[1406] The server generates appropriate suggestions for business improvement based on the results of sentiment analysis. For example, if negative emotions are detected, it makes specific suggestions to alleviate that dissatisfaction. If positive emotions are detected, it makes suggestions to maintain that level of satisfaction.

[1407] Specific example

[1408] When a worker says to the robot, "This machine is difficult to operate," the robot collects the audio and sends it to a server. The server converts the audio to text and performs sentiment analysis. If the analysis result is "negative," it suggests, "To address your dissatisfaction with this task, I propose reviewing the work procedure."

[1409] Example of a prompt

[1410] Worker: This machine is difficult to operate.

[1411] Robot: Recognized text: This machine is difficult to operate.

[1412] Robot: Proposal: To address dissatisfaction with this task, I propose reviewing the work procedure.

[1413] In this way, the system can efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate improvements to operations.

[1414] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1415] Step 1:

[1416] Users input their complaints via voice.

[1417] The user voice-inputs their complaints through a microphone. The input voice data is then transmitted from the microphone to the server.

[1418] Step 2:

[1419] The server converts the audio data into text data.

[1420] The server uses the speech_recognition library to convert received audio data into text data. The input is audio data, and the output is text data.

[1421] Step 3:

[1422] The server performs sentiment analysis on text data.

[1423] The server uses a sentiment analysis model from the transformers library to analyze the sentiment of text data. The input is text data, and the output is sentiment labels (positive, negative, neutral).

[1424] Step 4:

[1425] The server generates suggestions for business improvement based on the results of sentiment analysis.

[1426] The server generates appropriate business improvement suggestions based on the results of sentiment analysis. For example, if a negative emotion is detected, it will provide specific suggestions to address that dissatisfaction. The input is the sentiment label, and the output is the business improvement suggestion.

[1427] Step 5:

[1428] The server provides users with suggestions for improving their work processes.

[1429] The server provides the user with the business improvement suggestions it has generated. Specifically, it displays or reads out the suggestions through the robot's display or voice output. The input is the business improvement suggestions, and the output is feedback to the user.

[1430] 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.

[1431] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[1432] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1433] 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.

[1434] [Third Embodiment]

[1435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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.

[1440] 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).

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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.

[1446] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1447] "Example of form 1"

[1448] One embodiment of the present invention is a system that provides a web-based interface for soliciting opinions related to specific business processes, as a means of collecting opinions for business improvement. This interface allows users to input their opinions and submit them to the system.

[1449] "Example of form 2"

[1450] Furthermore, as a countermeasure in case no suggestions for business improvement are provided, an interface for soliciting complaints related to specific tasks will also be offered. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[1451] "Example of form 3"

[1452] Furthermore, as a measure for situations where there isn't time to provide advice on business process improvement, an interface for soliciting complaints related to specific tasks will also be provided. This interface allows users to input their complaints and submit them to the system. Specifically, users can submit their complaints by entering their complaints related to tasks into a text box and clicking the submit button.

[1453] The following describes the processing flow for each example of the form.

[1454] "Example of form 1"

[1455] Step 1: The user accesses a web-based interface.

[1456] Step 2: Users enter their opinions related to a specific task.

[1457] Step 3: The user submits their comments to the system.

[1458] "Example of form 2"

[1459] Step 1: The user accesses a web-based interface.

[1460] Step 2: Users enter complaints related to specific tasks.

[1461] Step 3: Send the user's submitted complaint to the system.

[1462] "Example of form 3"

[1463] Step 1: The user accesses a web-based interface.

[1464] Step 2: Users enter complaints related to specific tasks.

[1465] Step 3: Send the user's submitted complaint to the system.

[1466] (Example 1)

[1467] Next, we will describe Embodiment 1 of Embodiment 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."

[1468] Conventional business improvement systems struggled to efficiently collect user feedback and complaints and propose appropriate improvement measures. Furthermore, when managers lacked the time to provide business improvement advice, user dissatisfaction accumulated, leading to decreased operational efficiency. Moreover, there was a lack of effective means to manage the collected feedback and complaints and derive appropriate improvement measures.

[1469] 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.

[1470] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing a web-based interface, means for users to input and submit opinions, means for storing the input opinions in a database, means for displaying the stored opinions on a management screen, and means for an administrator to input prompt sentences into a generating AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[1471] "Methods for soliciting opinions on business process improvement" refers to a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business processes.

[1472] "A means of soliciting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that collects complaints and problems on behalf of users when they do not submit suggestions for business improvement.

[1473] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function for collecting complaints and problems from users when administrators do not have time to provide advice on business improvement.

[1474] "Means of providing a web-based interface" refers to a function that provides web pages and applications that users can access via the internet.

[1475] "A means for users to input and submit opinions" refers to a function that allows users to input their own opinions and suggestions and send them to the system.

[1476] "Means for saving submitted opinions to a database" refers to a function for recording and saving opinions and suggestions submitted by users in a database.

[1477] "Means for displaying saved opinions on the administration screen" refers to a function that displays opinions and suggestions saved in the database on the administration screen so that administrators can review them.

[1478] "A means for administrators to input prompts into a generation AI model and obtain suggestions" refers to a function that allows administrators to input specific questions or instructions into a generation AI model and receive suggestions or answers from the AI ​​based on those inputs.

[1479] Modes for carrying out the invention

[1480] This invention is a system for efficiently collecting opinions on business improvement and proposing appropriate improvement measures. A specific embodiment of this system is described below.

[1481] System Configuration

[1482] server

[1483] The server provides a web-based interface using web server software such as Apache or Nginx. The server has the functionality to receive user feedback and store it in a database. The database uses a database management system such as MySQL or PostgreSQL.

[1484] terminal

[1485] The terminal is a device that allows users to access a web interface and enter their opinions. The terminal connects to the server using a browser, enters opinions into an opinion input form, and sends the data to the server by pressing the submit button.

[1486] User

[1487] Users utilize the system to provide feedback on business improvements. Users access the web interface, enter their feedback into the feedback form, and submit their opinions by clicking the submit button.

[1488] Program processing

[1489] The server provides a webpage for the user to access. This webpage is created using HTML, CSS, and JavaScript. When the user enters their opinion and presses the submit button, the device sends the input data to the server as an HTTP POST request.

[1490] The server analyzes the received data and stores it in a database. The stored opinions are displayed on an administration screen accessible to administrators. This administration screen is built using server-side scripting languages ​​such as PHP and Python.

[1491] Administrators review user feedback on the administration screen and, if necessary, input prompts into the AI-generating model. Based on the input prompts, the AI-generating model suggests appropriate improvements and next actions.

[1492] Specific example

[1493] For example, consider a scenario where a user submits a request to "consider introducing a new project management tool." The user accesses the web interface, enters this information into the feedback form, and clicks the submit button. The server receives this data and saves it to the database.

[1494] The administrator reviews this feedback in the administration panel and enters the following prompt for the generating AI model:

[1495] A user has submitted feedback requesting that we consider implementing a new project management tool. Please advise on how to handle this feedback and what improvements should be proposed.

[1496] The generative AI model will suggest appropriate improvements based on this prompt. For example, it might return a suggestion such as, "Introducing a new project management tool could improve the efficiency of task management. Specifically, consider using a task management tool."

[1497] In this way, it becomes possible to efficiently collect feedback from users and quickly propose appropriate improvement measures.

[1498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1499] Step 1:

[1500] The user accesses the web interface.

[1501] Specific action: The user enters a URL into the browser's address bar and presses the Enter key.

[1502] Input: Access request from the user's browser.

[1503] Output: Display of a webpage.

[1504] Data Processing / Calculation: The server uses web server software such as Apache or Nginx to send HTML, CSS, and JavaScript files corresponding to the specified URL to the user's browser.

[1505] Step 2:

[1506] The user enters their opinion into the feedback form and presses the submit button.

[1507] Specific operation: The user enters their opinion using the keyboard and clicks the submit button with the mouse.

[1508] Input: User-submitted opinions.

[1509] Output: Click event of the submit button.

[1510] Data processing / calculation: The browser uses JavaScript to retrieve user input data and sends it to the server as an HTTP POST request.

[1511] Step 3:

[1512] The terminal sends the input data to the server.

[1513] Specific operation: The device sends data using the JavaScript fetch API.

[1514] Input: User-submitted opinion data.

[1515] Output: HTTP POST request to the server.

[1516] Data processing / calculation: The terminal converts the user-entered opinion data into JSON format and includes it in the body of an HTTP POST request, which is then sent to the server.

[1517] Step 4:

[1518] The server saves the data it receives to the database.

[1519] Specific operation: The server uses the Flask framework in Python to receive data and SQLAlchemy to save it to the database.

[1520] Input: Opinion data contained in the body of an HTTP POST request.

[1521] Output: Inserting data into the database.

[1522] Data processing / calculation: The server analyzes the received opinion data and inserts it into the corresponding table in the database.

[1523] Step 5:

[1524] The server displays the opinions saved in the administration panel.

[1525] Specific operation: The server uses an HTML template engine to generate an administration screen and display the saved comments.

[1526] Input: Opinion data stored in the database.

[1527] Output: HTML page of the administration screen.

[1528] Data processing / calculation: The server retrieves opinion data from the database, embeds it in an HTML template, and generates the administration screen.

[1529] Step 6:

[1530] The administrator reviews the feedback on the management screen and, if necessary, enters prompt text into the generated AI model.

[1531] Specific action: The administrator enters a prompt message into the text box on the administration screen and clicks the submit button.

[1532] Input: The prompt text entered by the administrator.

[1533] Output: Request to the generative AI model.

[1534] Data Processing / Calculation: Converts the prompt text entered by the administrator into an appropriate format for sending to the AI ​​model.

[1535] Step 7:

[1536] The generative AI model returns a suggestion.

[1537] Specific operation: The generation AI model generates text based on the input prompt and displays it to the administrator.

[1538] Input: The prompt text entered by the administrator.

[1539] Output: Suggested text from the generative AI model.

[1540] Data Processing / Calculation: The generative AI model analyzes prompt text and generates text that suggests appropriate improvements and next actions.

[1541] (Application Example 1)

[1542] Next, we will describe Application Example 1 of Form 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."

[1543] Traditional systems for soliciting feedback on business improvements had limited means for employees to submit opinions, making it particularly difficult to submit feedback within the factory. Furthermore, the lack of a system for efficiently transmitting feedback to a cloud-based database prevented managers from quickly reviewing the feedback.

[1544] 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.

[1545] In this invention, the server includes means for soliciting suggestions for business improvement, means for soliciting complaints as a countermeasure when no suggestions for business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for being installed on machinery in the factory and providing an interface for employees to input suggestions for business improvement, and means for transmitting the suggestions entered by employees to a cloud-based database. This makes it possible for employees to easily submit suggestions for business improvement within the factory, for those suggestions to be quickly transmitted to a cloud-based database, and for managers to efficiently review them.

[1546] A "means for soliciting opinions on business improvement" refers to a system that provides an interface for employees to submit suggestions for improving the efficiency and performance of their work.

[1547] A "means of collecting complaints" refers to a system that provides an interface for employees to submit complaints or problems regarding their work.

[1548] "Means installed on machinery within a factory to provide an interface for employees to input suggestions for improving operations" refers to a system installed on machinery within a factory that includes input devices such as touchscreens and keyboards that allow employees to directly input their suggestions.

[1549] "A means of sending employee-submitted opinions to a cloud-based database" refers to a system that sends employee-submitted opinions to a cloud-based database via the internet, allowing administrators to review them later.

[1550] A "cloud-based database" is a database stored on a remote server accessible via the internet, allowing multiple users to access, manage, and share data simultaneously.

[1551] A system for carrying out this invention includes means for providing an interface to be installed on machinery in a factory, and means for transmitting employee input to a cloud-based database. Specific embodiments of this system are described below.

[1552] System Configuration

[1553] The system consists of the following main components:

[1554] 1. Interfaces installed on machinery within the factory:

[1555] Hardware: Touchscreen display, keyboard, robots in the factory

[1556] Software: Python, Tkinter library, requests library

[1557] 2. Cloud-based databases:

[1558] Hardware: Remote server

[1559] Software: Database management systems (e.g., MySQL, PostgreSQL)

[1560] Program processing

[1561] The server provides an interface for employees to input feedback for improving work processes. When employees input feedback using a touchscreen display or keyboard, the feedback is retrieved by a Python program. The retrieved feedback is then sent to a cloud-based database as an HTTP POST request using the requests library.

[1562] Data processing and data calculation

[1563] Data Processing: Employee feedback is obtained in text format. The entered text is converted to the appropriate format and prepared as the payload for the HTTP request.

[1564] Data Processing: Opinions submitted to a cloud-based database are stored by the database management system and can be accessed and reviewed by administrators later.

[1565] Specific example

[1566] For example, consider a scenario where a factory employee inputs a suggestion such as "We would like to see new sensors installed to adjust the speed of the production line," and clicks the submit button. In this case, the suggestion entered by the employee is passed to a Python program via a touchscreen display and sent to a cloud-based database using the requests library.

[1567] Example of a prompt

[1568] Please enter your suggestion, "We would like to see new sensors installed to adjust the speed of the production line," and click the submit button.

[1569] In this way, employees can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by managers.

[1570] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1571] Step 1:

[1572] Users input suggestions for business improvement using a touchscreen display or keyboard installed on machinery within the factory. The input suggestions are sent to a terminal in text format. The input data is the text of the user's suggestions, and the output data is the text data stored on the terminal.

[1573] Step 2:

[1574] The terminal receives the input comments and converts them into an appropriate format. Specifically, it prepares the text data as the payload for an HTTP POST request. The input data is the text data stored on the terminal, and the output data is the data prepared as the payload for the HTTP request.

[1575] Step 3:

[1576] The device uses the requests library to send an HTTP POST request to a cloud-based database. The input data is the data prepared as the payload of the HTTP request, and the output data is the opinion data sent to the cloud-based database.

[1577] Step 4:

[1578] The server receives opinion data sent to a cloud-based database and stores it in the database management system. The input data is the opinion data sent to the cloud-based database, and the output data is the opinion data stored in the database.

[1579] Step 5:

[1580] The server provides data through a database management system so that administrators can review the opinion data later. The input data is opinion data stored in the database, and the output data is opinion data provided in a format accessible to administrators.

[1581] In this way, users can easily submit suggestions for improving operations within the factory, and these suggestions are quickly sent to a cloud-based database for efficient review by administrators.

[1582] (Example 2)

[1583] Next, we will describe Example 2 of the Form 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."

[1584] Traditional business improvement systems were inadequate in situations where user feedback was lacking or there wasn't enough time to provide business improvement advice. Furthermore, the lack of efficient means to collect and analyze user complaints made it difficult to implement concrete measures for business improvement. This resulted in delays in business efficiency and improvement.

[1585] 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.

[1586] In this invention, the server includes means for the user to input a complaint, means for the user to transmit a complaint, means for the server to receive the complaint, means for the server to store the complaint in a database, and means for the server to analyze the complaint data. This makes it possible to efficiently collect and analyze complaints from users.

[1587] "Means for soliciting opinions on business process improvement" refers to interfaces and functions for collecting opinions from users regarding improvements to business processes.

[1588] "Methods for soliciting complaints as a countermeasure when no suggestions for business improvement are provided" refers to interfaces or functions for collecting complaints from users regarding business operations.

[1589] "Means for users to input complaints" refers to text boxes or input fields where users can enter their complaints in text format.

[1590] "Means for users to submit complaints" refers to a submit button or function that allows users to send their submitted complaints to the system.

[1591] "Means by which a server receives complaints" refers to the functions or processes that a server uses to receive complaints sent by users.

[1592] "Means by which a server stores complaints in a database" refers to the functions or processes used to store received complaints in a database.

[1593] "Means for servers to analyze complaint data" refers to functions and processes for analyzing complaint data stored in a database and extracting trends and frequently occurring keywords.

[1594] This invention is a system for collecting complaints related to specific tasks when no suggestions for business improvement are provided, and then analyzing them. The following describes a specific implementation of this system.

[1595] Hardware and software to be used

[1596] Hardware: Servers, user terminals (PCs, smartphones, etc.)

[1597] Software: Web browsers, web servers (Apache, Nginx, etc.), backend frameworks (Django, Flask, etc.), databases (MySQL, PostgreSQL, etc.)

[1598] System Overview

[1599] This system provides an interface for users to input work-related complaints and send them to a server. The server stores the received complaints in a database and performs periodic analysis.

[1600] User actions

[1601] Users access the system interface using a web browser. The interface displays a text box where users enter work-related complaints. Once they have finished entering their complaints, they click the submit button to send them to the server.

[1602] Specific example

[1603] The user types "The meeting is too long" into the text box and clicks the submit button.

[1604] Server Processing

[1605] The server receives the HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) handles this process. The extracted complaint text is stored in a database. Along with the complaint text, the database also stores metadata such as the date and time of submission and the user ID.

[1606] Data analysis

[1607] The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[1608] Specific example

[1609] The analysis results show that many users complain that "meetings are too long."

[1610] Examples of prompts for generative AI models

[1611] "Analyze the user-submitted complaints about their work and identify the most common types of complaints."

[1612] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[1613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1614] Step 1:

[1615] Users enter their complaints.

[1616] Specific operation: The user opens a web browser and accesses the system interface. The interface displays a text box where the user enters work-related complaints.

[1617] Input: User-entered complaint text (e.g., "The meeting is too long")

[1618] Output: Complaint text entered in the text box

[1619] Step 2:

[1620] User submits complaint

[1621] Specific operation: The user enters a complaint into a text box and then clicks the submit button. When the submit button is clicked, the entered complaint is sent to the web server as an HTTP POST request.

[1622] Input: Complaint text entered in the text box

[1623] Output: Complaint text sent as an HTTP POST request

[1624] Step 3:

[1625] The server receives complaints.

[1626] Specific operation: The server receives an HTTP POST request and extracts the complaint text contained in the request body. The backend framework (e.g., Django, Flask) is responsible for this process.

[1627] Input: Complaint text sent as an HTTP POST request

[1628] Output: Extracted complaint text

[1629] Step 4:

[1630] The server saves complaints to the database.

[1631] Specific operation: The server saves the extracted complaint text to a database. The database also stores metadata such as the date and time of submission and the user ID, along with the complaint text.

[1632] Input: Extracted complaint text, date and time of submission, user ID, and other metadata.

[1633] Output: Complaint text and metadata stored in the database

[1634] Step 5:

[1635] The server analyzes the performance data.

[1636] Specific operation: The server periodically analyzes complaint data stored in the database. Text mining techniques are used to extract complaint trends and frequently occurring keywords. Generative AI models are used to analyze how often specific complaints occur.

[1637] Input: Complaint data stored in the database

[1638] Output: Trends in dissatisfaction, frequently occurring keywords, and analysis results.

[1639] In this way, users can easily send their work-related complaints to the system, which the server receives and analyzes, enabling concrete measures to be taken to improve operations.

[1640] (Application Example 2)

[1641] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1642] Traditional business improvement systems struggled to efficiently collect employee feedback and complaints. In particular, there was a lack of effective means to gather complaints when employees didn't offer suggestions for improvement or when there wasn't enough time to provide advice. Furthermore, within factories, the lack of an easily accessible interface for employees to report complaints meant that sufficient feedback for business improvement wasn't being obtained.

[1643] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for providing an interface for inputting and transmitting complaints related to specific tasks, and means for transmitting complaints to the server. This provides an environment in which employees can easily report complaints and enables the efficient collection of feedback for business improvement.

[1644] "Methods for soliciting opinions on business improvement" refers to methods or devices for collecting suggestions and opinions from employees regarding the efficiency and improvement of business operations.

[1645] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints about work when suggestions for business improvement cannot be obtained from employees.

[1646] "A means of collecting complaints as a countermeasure when there is no time to provide advice on improving work processes" refers to a method or device for collecting complaints from employees about their work when there is insufficient time to provide advice on improving work processes.

[1647] "Means of providing an interface for inputting and submitting complaints related to specific tasks" refers to a method or device that provides a user interface for employees to input complaints related to specific tasks and submit them to a system.

[1648] "Means for sending complaints to the server" refers to methods or devices for sending complaints entered by employees to a server.

[1649] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints regarding work improvements. Specifically, it constructs a system that allows employees working in a factory to easily report work-related complaints through a robot.

[1650] System program

[1651] This system uses the Python Tkinter library to create a GUI. It will be installed on a factory robot equipped with a touchscreen, providing an interface for employees to input and submit complaints.

[1652] Program Processing Description

[1653] The server processes data using the following hardware and software:

[1654] Hardware: Factory robots equipped with touchscreens

[1655] Software: Python, Tkinter library

[1656] The server retrieves the employee's complaint from the text box and sends it to the server. The specific process is as follows:

[1657] 1. Employees enter their complaints using a touchscreen.

[1658] 2. The entered complaint will be displayed in the text box.

[1659] 3. Clicking the submit button will send your complaint to the server.

[1660] 4. The server saves the received complaints to the database and notifies the administrator.

[1661] Specific example

[1662] If an employee working in the factory finds a new machine too complicated to operate and inefficient, they can use the robot's touchscreen to input their complaint as follows:

[1663] "The new machine is complicated to operate, which makes work inefficient."

[1664] Employees click the submit button, and their complaint is sent to the system. This information reaches administrators, who then consider solutions.

[1665] Example of a prompt

[1666] "Create an application that provides an interface for collecting complaints about work efficiency within the factory. Users should be able to input their complaints and submit them to the system by clicking a submit button."

[1667] In this way, it becomes possible to provide an environment where employees can easily report complaints and to efficiently collect feedback for improving operations.

[1668] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1669] Step 1:

[1670] The user enters their complaint using the touchscreen.

[1671] Input: Text data (content of complaint) entered by the user on the touchscreen.

[1672] Output: The complaints displayed in the text box.

[1673] Specific operation: The user enters their complaint using the keyboard on the touchscreen, and the content is displayed in a text box in real time.

[1674] Step 2:

[1675] The user clicks the submit button.

[1676] Input: The user's action of clicking the submit button.

[1677] Output: An event indicating that the submit button was clicked.

[1678] Specific action: A click event occurs when the user taps the send button on the touchscreen.

[1679] Step 3:

[1680] The device retrieves the complaint details from the text box.

[1681] Input: The complaint entered in the text box.

[1682] Output: Text data of the collected complaints.

[1683] Specific operation: The terminal reads the contents of the text box and stores that text data in a variable.

[1684] Step 4:

[1685] The device sends the complaint to the server.

[1686] Input: Text data of the complaints received.

[1687] Output: Text data of the complaint sent to the server.

[1688] Specific operation: The terminal generates an HTTP request and sends text data containing the complaint to the server.

[1689] Step 5:

[1690] The server saves the complaints it receives to a database.

[1691] Input: Text data of the complaint sent to the server.

[1692] Output: Records of complaints stored in the database.

[1693] Specific operation: The server analyzes the complaint received and saves it as a new record in the database.

[1694] Step 6:

[1695] The server notifies the administrator of the complaint.

[1696] Input: Records of complaints stored in the database.

[1697] Output: Notification message sent to the administrator.

[1698] Specific operation: The server notifies the administrator of the complaints based on the complaints stored in the database via email or a notification system.

[1699] By following these steps, it becomes possible to provide an environment where users can easily report complaints and efficiently collect feedback for business improvement.

[1700] (Example 3)

[1701] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1702] Conventional business improvement systems lack the means to address situations where suggestions for business improvement are not provided or where there is insufficient time to offer advice. Furthermore, they struggle to efficiently collect and analyze user complaints and provide concrete advice for business improvement. As a result, the effectiveness of business improvement is limited, and improving user satisfaction is difficult.

[1703] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1704] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for users to input complaints, means for a terminal to send the inputted complaints to the server, means for the server to receive the complaints and store them in a database, means for the server to analyze the stored complaints, and means for the server to send prompt messages to a generating AI model to generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[1705] "Methods for soliciting opinions on business improvement" refers to a function that provides an interface for collecting improvement suggestions related to business operations from users.

[1706] "A means of collecting complaints as a countermeasure when no suggestions for business improvement are submitted" refers to a function that provides an interface for collecting complaints about work processes when users do not submit suggestions for business improvement.

[1707] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from users about their work when there is insufficient time to provide advice on business improvement.

[1708] "A means for users to input complaints" refers to a function that provides an interface for users to input their complaints about their work in text format.

[1709] "Means for sending user-entered complaints to a server" refers to a communication function for sending user-entered complaints to a server via a network.

[1710] "A means for a server to receive complaints and store them in a database" refers to a function that allows a server to receive complaints sent by users and record them in a database.

[1711] "Means for analyzing stored complaints on the server" refers to a function that analyzes complaint data stored in a database and extracts specific patterns or trends.

[1712] "A means by which the server sends prompt messages to the generating AI model and generates business improvement advice" refers to a function in which the server sends prompt messages to the generating AI model based on the analysis results, and the generating AI model generates business improvement advice.

[1713] Modes for carrying out the invention

[1714] This invention is a system that efficiently collects and analyzes opinions and complaints regarding business improvement and provides specific advice for business improvement. A specific embodiment of this system is described below.

[1715] System Configuration

[1716] This system consists of the following main components:

[1717] 1. User Interface: An interface for users to input opinions and complaints related to their work. It operates on a web browser and is built using HTML, CSS, and JavaScript.

[1718] 2. Terminal: A device used to send data entered by the user to the server. Typically, a personal computer or smartphone is used.

[1719] 3. Server: Receives data sent from users, stores it in a database, and performs analysis. Uses the Flask framework to process HTTP requests and Python to analyze the data.

[1720] 4. Database: Data storage for saving opinions and complaints submitted by users. Relational databases such as MySQL are used.

[1721] 5. Generative AI Model: An artificial intelligence model that generates business improvement advice by sending prompt messages based on analysis results from the server.

[1722] Hardware and software details

[1723] User Interface: HTML, CSS, JavaScript

[1724] Devices: PC, smartphone

[1725] Server: Flask framework, Python

[1726] Database: MySQL

[1727] Generative AI model: A specific generative AI model (e.g., GPT-3)

[1728] Specific example

[1729] Consider a scenario where a user submits a complaint that "the meeting is too long." The user types "the meeting is too long" into a text box on their web browser and clicks the submit button. This action sends the user's complaint from their device to the server.

[1730] The server uses the Flask framework to receive HTTP POST requests, parse the received data, and save it to a MySQL database. For example, it executes the SQL query INSERT INTO complaints (text, timestamp) VALUES ('The meeting is too long', '2023-10-01 12:00:00').

[1731] Next, the server uses the Python pandas library to read complaint data from the database and perform analysis. For example, it calculates the frequency of occurrence of specific keywords to understand what kinds of complaints are most common.

[1732] Finally, the server sends a prompt message to the generating AI model based on the analysis results. For example, in response to the complaint "Meetings are too long," the server sends the prompt message "We have received a complaint that meetings are too long. Please generate advice for improving the process in response to this complaint." to the generating AI model. Based on this prompt message, the generating AI model generates advice for improving the process and returns it to the server. The server then provides this advice to the user.

[1733] In this way, it becomes possible to efficiently collect and analyze user complaints and provide specific advice for business improvement. The flow of the specific process in Example 3 will be explained using Figure 15.

[1734] Step 1:

[1735] Users enter their complaints.

[1736] The user accesses the system interface using a web browser. The interface displays a text box for entering work-related complaints and a submit button. The user enters their complaint in the text box and clicks the submit button. The input data is the text of the complaint entered by the user. The output data is the event that the submit button was clicked.

[1737] Step 2:

[1738] The terminal sends the entered complaint to the server.

[1739] When the user clicks the submit button, the device uses JavaScript to send the entered complaint to the server as an HTTP POST request. This request includes the text of the complaint entered by the user. The input data is the text of the complaint entered by the user. The output data is the HTTP POST request sent to the server.

[1740] Step 3:

[1741] The server receives the complaint and saves it to the database.

[1742] The server uses the Flask framework to receive HTTP POST requests. It parses the received complaint text and saves it to a MySQL database. During saving, a new record is added to the database table, recording the complaint text and a timestamp. The input data is the complaint text contained in the HTTP POST request. The output data is the new record saved in the database.

[1743] Step 4:

[1744] The server analyzes the stored complaints.

[1745] The server periodically analyzes complaints stored in the database. The analysis uses the Python pandas library. For example, it calculates the frequency of specific keywords to understand which complaints are most common. The input data is text data of complaints retrieved from the database. The output data is the keyword frequency and complaint trends as a result of the analysis.

[1746] Step 5:

[1747] The server sends prompt messages to the generated AI model, which then generates advice for business improvement.

[1748] The server sends prompt messages to the generating AI model based on the analysis results. These prompt messages include specific details of the user's dissatisfaction. The generating AI model generates business improvement advice based on the received prompt messages and returns it to the server. The server then provides this advice to the user. The input data consists of the analysis results and the generated prompt messages. The output data is the business improvement advice returned by the generating AI model.

[1749] (Application Example 3)

[1750] Next, we will describe application example 3 of form example 3. 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."

[1751] Traditional business improvement systems struggled to efficiently collect and analyze employee feedback and complaints. Furthermore, they lacked effective means of addressing employee dissatisfaction, even when there wasn't time to provide business improvement advice. As a result, feedback for business improvement wasn't fully utilized, hindering improvements in operational efficiency.

[1752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1753] In this invention, the server includes means for providing an interface for inputting complaints, means for storing the input complaints in a database, means for inputting the stored complaints into a generative AI model and generating analysis results, and means for notifying the administrator of the analysis results. This makes it possible to efficiently collect complaints from employees, analyze them using the generative AI model, provide rapid feedback to administrators, and take concrete actions to improve operations.

[1754] "Means for soliciting opinions on business improvement" refers to methods or devices for collecting opinions from employees regarding improvements to business operations.

[1755] "Methods for collecting complaints as a countermeasure when no suggestions for business improvement are provided" refers to methods or devices for collecting complaints from employees when suggestions for business improvement cannot be obtained from them.

[1756] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to provide advice on business improvement.

[1757] "Means of providing an interface for inputting complaints" refers to methods or devices that provide a user interface for employees to input complaints.

[1758] "Means for saving submitted complaints to a database" refers to methods or devices for saving employee-submitted complaints to a database.

[1759] "Means for inputting stored complaints into a generation AI model and generating analysis results" refers to a method or apparatus for inputting complaints stored in a database into a generation AI model and generating analysis results.

[1760] "Means for notifying the administrator of analysis results" refers to methods or devices for notifying the administrator of analysis results generated by the generative AI model.

[1761] The system for implementing this invention is configured as an application installed on robots working in a factory. This system efficiently collects employee complaints, analyzes them using a generative AI model, and notifies managers of the results, thereby promoting business improvement.

[1762] Hardware and software to use

[1763] Hardware: Factory robots, touchscreen displays

[1764] Software: Python, Flask (web framework), SQLite (database), OpenAI GPT-3 (generative AI model)

[1765] System configuration and operation

[1766] 1. Display of the complaint input interface:

[1767] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. Employees can use this interface to enter their complaints.

[1768] 2. Collection of dissatisfaction data:

[1769] When a user enters their complaint into a text box and clicks the submit button, the server receives the data and saves it to an SQLite database.

[1770] 3. Analysis of dissatisfaction:

[1771] The server periodically retrieves complaint data from the SQLite database and inputs it as prompts into OpenAI GPT-3. The generative AI model generates analysis results based on the input complaints.

[1772] 4. Report to the administrator:

[1773] The server notifies the administrator of the generated analysis results via email. The email is sent using the Python smtplib library.

[1774] Specific example

[1775] For example, a factory employee might type "Machine maintenance is delayed" and click the submit button. This complaint is collected by the server and stored in an SQLite database. The server then inputs this complaint into OpenAI GPT-3 as the following prompt:

[1776] User complaint: Machine maintenance is delayed.

[1777] Prompt to the generating AI model: Generate improvement suggestions for this complaint.

[1778] The AI ​​model generates analysis results such as "The maintenance schedule needs to be reviewed," and the server notifies the administrator of the result via email.

[1779] In this way, by efficiently collecting employee complaints and analyzing them using generative AI models, it becomes possible to provide rapid feedback to managers and take concrete actions to improve operations.

[1780] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1781] Step 1:

[1782] The server uses Flask to launch a web server and provides a web page that displays a text box and a submit button on the touchscreen display. The user enters complaints using this interface. The input is the complaint entered by the user in the text box, and the output is the complaint data sent to the server when the submit button is clicked.

[1783] Step 2:

[1784] The server receives the complaint data entered by the user when they click the submit button. The input is the complaint data submitted by the user, and the output is the storage of that complaint data in an SQLite database. Specifically, the server inserts the received complaint data into the database using SQL statements.

[1785] Step 3:

[1786] The server periodically retrieves complaint data from an SQLite database. The complaint data stored in the database serves as input, and the retrieved complaint data is fed into the generating AI model as output. Specifically, the server executes SQL queries to retrieve the complaint data from the database.

[1787] Step 4:

[1788] The server inputs the acquired complaint data into a generative AI model and generates analysis results. The input consists of the acquired complaint data and prompt statements, and the output is the analysis results generated by the generative AI model. Specifically, the server calls the OpenAI GPT-3 API and inputs the complaint data as prompt statements.

[1789] Step 5:

[1790] The server notifies the administrator of the generated analysis results. The input is the analysis results generated by the generative AI model, and the output is a notification email sent to the administrator. Specifically, the server uses the Python smtplib library to send the email containing the analysis results to the administrator.

[1791] In this way, employee complaints can be efficiently collected, analyzed using a generative AI model, and the results can be communicated to managers, enabling concrete actions to be taken to improve operations.

[1792] 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.

[1793] "Example of form 1"

[1794] One embodiment of the present invention incorporates an emotion engine. This emotion engine analyzes emotions from opinions and complaints entered by the user. Specifically, it analyzes the user's opinions on business improvement.

[1795] When input is received, the emotion engine analyzes the emotional elements of the opinion. For example, if a user inputs the opinion, "This task takes too long," the emotion engine analyzes the user's feelings of dissatisfaction and stress from that opinion. This analysis result is used as reference information when making suggestions for improving the work process.

[1796] "Example of form 2"

[1797] Furthermore, the emotion engine also has the function of tracking changes in the user's emotions. Specifically, if a user inputs opinions or complaints multiple times, the emotion engine tracks the changes in the user's emotions from those inputs. For example, it tracks changes in emotions such as a user feeling dissatisfied when they first input an opinion, but feeling satisfied after several inputs. The results of this tracking of emotional changes are used as reference information when updating suggestions for business improvement. "Example Form 3"

[1798] Furthermore, the emotion engine takes user emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement suggestions based on the analysis of user emotions and the tracking of changes in those emotions. For example, for tasks that users are dissatisfied with, it makes suggestions to resolve that dissatisfaction. Conversely, for tasks that users are satisfied with, it makes suggestions to maintain that satisfaction. In this way, the emotion engine enables business improvement suggestions that take user emotions into account.

[1799] The following describes the processing flow for each example of the form.

[1800] "Example of form 1"

[1801] Step 1: Users input their opinions on improving the business process.

[1802] Step 2: The emotion engine analyzes emotional elements from the user's input.

[1803] Step 3: Based on the analysis results, propose business improvements.

[1804] "Example of form 2"

[1805] Step 1: Users input their opinions and complaints multiple times.

[1806] Step 2: The emotion engine tracks changes in emotion based on user input.

[1807] Step 3: Update business improvement proposals based on the results of tracking emotional changes.

[1808] "Example of form 3"

[1809] Step 1: The emotion engine proposes business improvements based on the analysis of the user's emotions and the tracking of changes in those emotions.

[1810] Step 2: For tasks that users are dissatisfied with, propose solutions to address those dissatisfactions.

[1811] Step 3: For tasks that users find satisfying, make suggestions to maintain that satisfaction.

[1812] (Example 1)

[1813] Next, we will describe Embodiment 1 of Embodiment 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."

[1814] Conventional business improvement systems lack effective means of collecting and analyzing user feedback and complaints, resulting in inappropriate business improvement proposals. Furthermore, because analysis does not take user emotions into account, it is difficult to create improvement proposals that reflect users' true intentions and feelings.

[1815] 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.

[1816] In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for transmitting the received opinions to an emotion analysis engine, means for the emotion analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for making business improvement proposals based on the analysis results. This makes it possible to effectively collect user opinions and complaints and to make business improvement proposals that reflect the user's true intentions and emotions through emotion analysis.

[1817] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for users to input and submit improvement suggestions related to business operations to the system.

[1818] A "means of collecting complaints" refers to a function that provides an interface for users to input and submit their complaints and problems regarding their work to the system.

[1819] "Means for receiving user input" refers to a function that allows a server to receive opinions submitted by users through a web interface.

[1820] "Means for sending received opinions to the sentiment analysis engine" refers to the function by which the server sends the user's opinions received to the sentiment analysis engine.

[1821] "A means by which an emotion analysis engine analyzes opinions and extracts emotional elements" refers to a function in which an emotion analysis engine analyzes a user's opinion and extracts emotional elements from it.

[1822] "Means for saving analysis results" refers to the function of saving the analysis results obtained from the emotion analysis engine to a database.

[1823] "A means of proposing business improvements based on analysis results" refers to a function that generates proposals for business improvements based on saved analysis results and notifies managers and relevant parties.

[1824] Modes for carrying out the invention

[1825] This invention is a system for effectively collecting and analyzing opinions on business improvement. A specific embodiment of this system is described below.

[1826] System Configuration

[1827] This system consists of three main elements: a server, terminals, and users. The server is responsible for soliciting suggestions for business improvement, analyzing the received suggestions, saving the analysis results, and proposing improvements. The terminals provide an interface for users to input their suggestions. Users input their suggestions through the terminals and send them to the system.

[1828] Hardware and software to be used

[1829] The server uses a standard web server and implements a sentiment analysis engine using Python's natural language processing library NLTK and the sentiment analysis library TextBlob. MySQL is used for the database, and Flask is used as the web framework. The terminal uses a web browser (e.g., Google Chrome) for user access.

[1830] System operation

[1831] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding work improvements into a text box and click the "Submit" button. For example, a user might enter, "This task takes too long."

[1832] The server receives the user's submitted opinion as an HTTP request. The server parses this request and extracts the text data of the opinion. The extracted text data of the opinion is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinion and extract emotions such as "dissatisfaction" and "stress." The analysis results are returned to the server in JSON format.

[1833] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes user opinions and their sentiment analysis results. Based on the saved analysis results, the server makes suggestions for business improvement. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator.

[1834] Examples of specific cases and prompt statements

[1835] As a concrete example, a user accesses a web interface and enters the text, "This task takes too long." The server receives this comment and sends it to the sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" and "stress," and returns the results to the server. The server saves these analysis results to a database and uses them later to suggest improvements to the process.

[1836] Example of a prompt:

[1837] "A user has submitted feedback for business improvement. Please use the sentiment engine to analyze the emotional elements of this feedback and save the results."

[1838] In this way, a system is realized in which servers, terminals, and users each fulfill their respective roles, effectively collecting and analyzing feedback for business improvement.

[1839] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1840] Step 1:

[1841] Users enter their opinions.

[1842] Users access the system's web interface using a web browser. They enter their opinions and complaints regarding business improvements into text boxes and click the "Submit" button. The input data is the text of the opinion entered by the user. The output is the text data of the submitted opinion.

[1843] Step 2:

[1844] The server receives the feedback.

[1845] The server receives the user's submitted opinion as an HTTP POST request. The server uses the Flask framework to parse the request and extract the opinion's text data. The input data is the HTTP POST request, and the output is the extracted opinion's text data.

[1846] Step 3:

[1847] The server sends feedback to the sentiment analysis engine.

[1848] The server sends the extracted opinion text data to the sentiment analysis engine. The sentiment analysis engine is implemented using NLTK, a Python natural language processing library, and TextBlob, a sentiment analysis library. The input data is the opinion text data, and the output is the data sent to the sentiment analysis engine.

[1849] Step 4:

[1850] The emotion analysis engine analyzes the opinion.

[1851] The sentiment analysis engine analyzes the text data of received opinions and extracts emotional elements. For example, it analyzes emotions such as "dissatisfaction" and "stress" from an opinion such as "This task takes too long." The input data is the text data of the opinion, and the output is the sentiment analysis result.

[1852] Step 5:

[1853] The server saves the analysis results.

[1854] The server receives the analysis results returned from the sentiment analysis engine and saves them to a MySQL database. The saved data includes the user's opinion and its sentiment analysis result. The input data is the sentiment analysis result, and the output is the data saved in the database.

[1855] Step 6:

[1856] The server makes suggestions for business improvements based on the analysis results.

[1857] The server makes suggestions for business improvements based on the saved analysis results. For example, if multiple users have similar complaints, it generates suggestions for reviewing the business process and notifies the administrator. The input data is the analysis results stored in the database, and the output is the generated business improvement suggestions.

[1858] (Application Example 1)

[1859] Next, we will describe Application Example 1 of Form 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."

[1860] Traditional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. Furthermore, they lacked the functionality to analyze the emotional elements of the collected opinions and complaints and provide real-time feedback to managers, making rapid business improvement difficult.

[1861] 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. In this invention, the server includes means for soliciting opinions on business improvement, means for soliciting complaints as a countermeasure when no opinions on business improvement are received, means for soliciting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of input opinions using an emotion analysis engine, means for notifying the analysis results in real time, and means for saving the analysis results. This makes it possible to efficiently collect opinions and complaints from workers, analyze the emotional elements, and provide feedback to managers in real time.

[1862] "A means of soliciting suggestions for business improvement" refers to a function that provides an interface for workers to input suggestions for improving their work processes.

[1863] "A means of soliciting complaints as a countermeasure when no suggestions for improving work processes are made" refers to a function that provides an interface for workers to input complaints about their work.

[1864] "A means of collecting complaints as a countermeasure when there is no time to provide advice on business improvement" refers to a function that provides an interface for collecting complaints from workers when there is insufficient time to provide advice on business improvement.

[1865] "Means for analyzing the emotional elements of input opinions using an emotion analysis engine" refers to software or algorithms for analyzing the emotional elements of opinions and complaints entered by workers.

[1866] The "means of notifying analysis results in real time" refer to a function that immediately notifies factory managers of the results analyzed by the emotion analysis engine.

[1867] "Means for saving analysis results" refers to a function for saving the results analyzed by the emotion analysis engine to a database or similar.

[1868] One embodiment of this invention is a system that collects opinions and complaints about work improvements from factory workers, analyzes those opinions using an emotion analysis engine, and provides feedback to managers in real time.

[1869] System Configuration

[1870] hardware

[1871] Server: A central computer that collects, analyzes, and stores data.

[1872] Terminal: A smartphone or tablet used by workers to input opinions and complaints.

[1873] Network: The internet or local network used to connect the server and the terminal.

[1874] software

[1875] Web-based interface: A user interface for workers to input opinions and complaints.

[1876] Emotion analysis engine: Software that uses NLTK (Natural Language Toolkit) to analyze the emotional elements of input opinions.

[1877] Database: A database using SQLite to store collected opinions and analysis results.

[1878] Processing flow

[1879] 1. Gathering Feedback: Workers use smartphones or tablets to access a web-based interface and input their opinions and complaints regarding work improvements.

[1880] 2. Data transmission: The submitted opinions and complaints are sent to the server via the network.

[1881] 3. Sentiment Analysis: The sentiment analysis engine on the server analyzes the emotional elements of the input opinions and complaints.

[1882] 4. Real-time notification: Analysis results are notified to factory managers in real time.

[1883] 5. Data Storage: Along with the analysis results, the opinions and complaints entered are stored in the database.

[1884] Specific example

[1885] For example, if a worker inputs feedback such as, "This machine breaks down frequently, causing delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." This analysis result is immediately notified to the factory manager and stored in the database.

[1886] Example of a prompt

[1887] Use an emotion analysis engine to analyze the opinions entered by ...

Claims

1. A means of receiving opinions or complaints from each worker in the factory regarding machine operation or the work environment, An emotion identification model includes a neural network trained to take the aforementioned opinion or dissatisfaction as input and output an emotion value representing each of the multiple emotions shown in the emotion map, inputs the worker's opinion or dissatisfaction into the neural network, obtains an emotion value representing each emotion shown in the emotion map, and calculates a score representing the worker's emotion based on each obtained emotion value, A server generates a prompt message instructing the generating AI to generate improvement proposals regarding the machine or work environment based on the input opinions or complaints and the analysis results of each worker's score, and a means for generating improvement proposals regarding the machine or work environment using the prompt message and the generating AI, For each worker, means for tracking the change in the worker's score, Includes, The means for generating improvement proposals involves the server generating a prompt message instructing the generation AI to generate improvement proposals regarding the machine or work environment based on the input opinions or complaints and the analysis results of changes in each worker's score, and then using the prompt message and the generation AI to generate improvement proposals regarding the machine or work environment. system.

2. The means further include being installed on machinery within the factory and providing an interface for the operator to input the opinion or complaint. The system according to claim 1.

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