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 technology]

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

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

[0004] Although we collect opinions on business improvement, there are issues with not receiving any opinions. Also, even if opinions are received, there is an issue with not having time to implement improvement measures or advice due to busy work schedules. [Means for solving the problem]

[0005] As a means for solving these problems, we propose a system that provides a means for collecting opinions on business improvement, a means for collecting complaints as a countermeasure when no opinions on business improvement are given, and a means for collecting complaints as a countermeasure when there is no time to give advice on business improvement. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

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

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

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

[0029] "Example 2"

[0030] Furthermore, as a countermeasure when no suggestions for business improvement are received, an interface for collecting complaints related to specific business operations is also provided. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[0031] "Example 3"

[0032] Furthermore, as a countermeasure for cases where there is no time to provide advice on business improvement, an interface is also provided for collecting complaints related to specific business operations. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

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

[0036] Step 2: The user enters their opinion related to a specific task.

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

[0038] "Example 2"

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

[0040] Step 2: The user enters a specific job-related complaint.

[0041] Step 3: The user submits their complaint to the system.

[0042] "Example 3"

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

[0044] Step 2: The user enters a specific job-related complaint.

[0045] Step 3: The user submits their complaint to the system.

[0046] Example 1

[0047] Next, a description will be given of 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."

[0048] Conventional business improvement systems have difficulty efficiently collecting user opinions and complaints and proposing appropriate improvement measures. Furthermore, if managers do not have time to provide advice on business improvement, users' dissatisfaction accumulates, resulting in a decline in business efficiency. Furthermore, there is a lack of means to effectively manage the collected opinions and complaints and derive appropriate improvement measures.

[0049] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0050] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting 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 send opinions, means for saving the input opinions in a database, means for displaying the saved opinions on an administration screen, and means for an administrator to input prompt sentences to the generative AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[0051] The "means for collecting opinions on business improvement" is a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business operations.

[0052] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function for collecting complaints and problems instead of users when they do not submit suggestions for business improvement.

[0053] The "means of collecting complaints as a measure when there is no time to give advice on business improvement" is a function for collecting complaints and problems from users when the administrator does not have time to give advice on business improvement.

[0054] A "means for providing a web-based interface" is a function for providing web pages and applications that can be accessed by users via the Internet.

[0055] "Means for users to input and send opinions" is a function that allows users to input their own opinions and suggestions and send them to the system.

[0056] The "means for saving input opinions in a database" is a function for recording and saving opinions and suggestions sent by users in a database.

[0057] The "means for displaying saved opinions on the management screen" is a function for displaying the opinions and suggestions saved in the database on the management screen so that the administrator can check them.

[0058] "A means for an administrator to input prompt statements into a generative AI model and obtain suggestions" is a function that allows an administrator to input specific questions or instructions into a generative AI model and obtain suggestions or answers from the AI ​​based on those questions or instructions.

[0059] MODE FOR CARRYING OUT THE INVENTION

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

[0061] System configuration

[0062] server

[0063] The server uses web server software such as Apache (registered trademark) or Nginx to provide a web-based interface. The server also has the functionality to receive opinions submitted by users and store them in a database. The database uses a database management system such as MySQL (registered trademark) or PostgreSQL.

[0064] Terminal

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

[0066] User

[0067] Users use the system to provide suggestions for business improvement. Users access the web interface, enter their suggestions in the suggestion input form, and submit their suggestions by pressing the send button.

[0068] Program processing

[0069] The server provides a web page for users to access. This web page 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 that can be accessed by administrators. This administration screen is built using server-side scripts such as PHP and Python.

[0071] The administrator checks user feedback on the management screen and, if necessary, inputs prompts into the generative AI model, which then suggests appropriate improvements and next actions based on the input prompts.

[0072] Specific examples

[0073] For example, consider a user who wants to input an opinion such as "Please consider introducing a new project management tool." The user accesses a web interface, enters this information into the opinion input form, and presses the submit button. The server receives this data and stores it in a database.

[0074] The administrator confirms this opinion in the management screen and enters the following prompt for the generative AI model:

[0075] A user has submitted a request to "consider introducing a new project management tool." How should we handle this request and what improvements should we suggest?

[0076] The generative AI model then suggests appropriate improvements based on the prompt, such as, "Task management efficiency could be improved by introducing a new project management tool. Specifically, consider using a task management tool."

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

[0078] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0079] Step 1:

[0080] The user accesses the web interface.

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

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

[0083] Output: A web page is displayed.

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

[0085] Step 2:

[0086] The user enters their opinion in the opinion input form and presses the send button.

[0087] Specific behavior: The user uses the keyboard to type in their opinion and then uses the mouse to click the submit button.

[0088] Input: User-entered opinion.

[0089] Output: Submit button click event.

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

[0091] Step 3:

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

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

[0094] Input: Opinion data entered by the user.

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

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

[0097] Step 4:

[0098] The server stores the received data in a database.

[0099] How it works: The server receives data using Python's Flask framework and stores it in a database using SQLAlchemy.

[0100] Input: Opinion data included in the body of the 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 saved opinions on the administration screen.

[0105] Specific operation: The server generates an administration screen using an HTML template engine and displays the saved opinions.

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

[0107] Output: HTML page of the admin panel.

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

[0109] Step 6:

[0110] The administrator checks the opinions on the management screen and, if necessary, inputs prompt text into the generative AI model.

[0111] Specific operation: The administrator enters a prompt text in the text box on the administration screen and clicks the send button.

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

[0113] Output: The request to the generative AI model.

[0114] Data processing / calculation: Converting the prompt text entered by the administrator into an appropriate format for sending to the generative AI model.

[0115] Step 7:

[0116] A generative AI model returns suggestions.

[0117] Specific operation: The generative 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 / computation: The generative AI model analyzes the prompt and generates text that suggests appropriate improvements or next actions.

[0121] (Application example 1)

[0122] Next, a description will be given of Application Example 1 of Embodiment 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] The previous system for collecting opinions for business improvement limited the means by which employees could submit their opinions, making it difficult to do so, especially within the factory. Furthermore, there was a lack of a mechanism for efficiently sending opinions to a cloud-based database, making it difficult for managers to quickly review the opinions.

[0124] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0125] In this invention, the server includes means for collecting suggestions for business improvement, means for collecting complaints as a countermeasure when no suggestions for business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice for business improvement, means that is installed on machines in the factory and provides an interface for employees to input suggestions for business improvement, and means for sending the suggestions input by employees to a cloud-based database. This enables employees to easily submit suggestions for business improvement in the factory, and the suggestions are quickly sent to the cloud-based database where managers can efficiently check them.

[0126] The "means for soliciting opinions on business improvement" is a system that provides an interface for employees to submit suggestions for improving or streamlining business operations.

[0127] A "grievance channel" is a system that provides an interface for employees to submit complaints or issues about their work.

[0128] "A means installed on machines in a factory that provides an interface for employees to input their opinions on business improvements" refers to a system that is installed on machines in a factory and includes input devices such as touch screens and keyboards that allow employees to directly input their opinions.

[0129] The "means for transmitting opinions entered by employees to a cloud-based database" refers to a system that transmits opinions entered by employees to a cloud-based database via the Internet, allowing managers to check them later.

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

[0131] A system for implementing the present invention includes a means for providing an interface installed on a machine in a factory and a means for transmitting opinions entered by employees to a cloud-based database. A specific embodiment of this system will be described below.

[0132] System configuration

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

[0134] 1. Interfaces installed on machines in the factory:

[0135] Hardware: Touchscreen displays, keyboards, factory robots

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

[0137] 2. Cloud-based database:

[0138] Hardware: Remote Server

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

[0140] Program processing

[0141] The server provides an interface for employees to input their suggestions for business improvement. Employees enter their suggestions using a touchscreen display or keyboard, and the suggestions are retrieved by a Python program. The retrieved suggestions are then sent to a cloud-based database as HTTP POST requests using the requests library.

[0142] Data processing and calculation

[0143] Data processing: The opinions entered by employees are obtained in text format. The entered text is converted into an appropriate format and prepared as the payload for the HTTP request.

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

[0145] Specific examples

[0146] For example, consider a scenario in which a factory worker types in their request, "I'd like a new sensor installed to adjust the speed of the production line," and clicks the submit button. In this case, the worker's request is passed to a Python program via a touchscreen display and then sent to a cloud-based database using the requests library.

[0147] Prompt Sentence Examples

[0148] Please enter your opinion such as "We would like a new sensor 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 the suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[0150] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0151] Step 1:

[0152] A user inputs their thoughts on business improvement using a touchscreen display or keyboard installed on a machine in a factory. The inputted thoughts are sent to a terminal in text format. The input data is the text of the thoughts input by the user, and the output data is the text data stored in the terminal.

[0153] Step 2:

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

[0155] Step 3:

[0156] The terminal uses the requests library to send an HTTP POST request to the 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 the opinion data sent to the cloud-based database and stores it in the database management system, where 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 the data through a database management system so that the administrator can later review the opinion data. The input data is the opinion data stored in the database, and the output data is the opinion data provided in a format accessible to the administrator.

[0161] In this way, users can easily submit suggestions for improving operations within the factory, and those suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[0162] Example 2

[0163] Next, a description will be given of 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 will be referred to as a "terminal."

[0164] Conventional business improvement systems were inadequate when users did not provide feedback or when there was no time to provide advice on business improvement. Furthermore, there was a lack of a means to efficiently collect and analyze user complaints, making it difficult to implement specific measures for business improvement. This resulted in problems such as delays in business efficiency and improvements.

[0165] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0166] In this invention, the server includes a means for users to input complaints, a means for users to transmit complaints, a means for the server to receive the complaints, a means for the server to store the complaints in a database, and a means for the server to analyze the complaint data, thereby enabling efficient collection and analysis of complaints from users.

[0167] "Means for collecting opinions on business improvement" refers to interfaces and functions for collecting opinions on business improvement from users.

[0168] "Means for collecting complaints as a countermeasure when no suggestions for business improvement are made" refers to interfaces and functions for collecting business-related complaints from users.

[0169] "Means for users to input complaints" refers to a text box or input field where users can enter their complaints in text form.

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

[0171] "Means by which the server receives complaints" refers to the functions and processes by which the server receives complaints sent by users.

[0172] "Means by which the server stores complaints in a database" refers to the functionality or process by which received complaints are stored in a database.

[0173] "Means by which the server analyzes the complaint data" refers to the functions and processes for analyzing the complaint data stored in the database and extracting trends and frequently occurring keywords.

[0174] This invention is a system for collecting and analyzing complaints related to a specific task when no suggestions for improving the task are made. A specific embodiment of this system will be described below.

[0175] Hardware and software used

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

[0177] Software: Web browser, Web server (Apache, Nginx, etc.), Backend framework (Django, Flask, etc.), Database (MySQL, PostgreSQL, etc.)

[0178] System Overview

[0179] The system provides an interface for users to input work-related complaints and send them to a server, which stores the complaints in a database and periodically analyzes them.

[0180] User operations

[0181] Users access the system interface using a web browser, which displays a text box where users can enter their work-related complaints. Once they have completed the input, they click a submit button to send the complaint to the server.

[0182] Specific examples

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

[0184] Server Processing

[0185] The server receives the HTTP POST request and extracts the complaint text from the request body. This process is handled by a backend framework (e.g., Django or Flask). The extracted complaint text is stored in a database. Along with the complaint text, metadata such as the submission date and user ID are also stored in the database.

[0186] Analyzing the data

[0187] The server periodically analyzes the complaint data stored in the database, uses text mining technology to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[0188] Specific examples

[0189] The server outputs the analysis result that there are many complaints that "meetings are too long."

[0190] Example prompts for generative AI models

[0191] "Analyze work-related complaints entered by users and identify the most common types of complaints."

[0192] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[0193] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0194] Step 1:

[0195] User enters complaint

[0196] Specific behavior: A user opens a web browser and accesses the system interface, which displays a text box where the user can enter their 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] What happens: The user enters their complaint into the text box and then clicks the submit button, which sends the complaint 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] Server receives complaint

[0206] What happens: The server receives the HTTP POST request and extracts the complaint text from the request body. This is handled by the backend framework (e.g., Django, Flask).

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

[0208] Output: Extracted complaint text

[0209] Step 4:

[0210] The server stores the complaint in a database

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

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

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

[0214] Step 5:

[0215] The server analyzes the complaint data

[0216] How it works: The server periodically analyzes the complaint data stored in the database, uses text mining techniques to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[0217] Input: Complaint data stored in the database

[0218] Output: Dissatisfaction trends, frequently occurring keywords, and analysis results

[0219] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[0220] (Application example 2)

[0221] Next, a description will be given of 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 will be referred to as a "terminal."

[0222] With conventional business improvement systems, it was difficult to efficiently collect opinions and complaints from employees, and there was a lack of means to properly collect complaints, especially when no suggestions for business improvement were made or when there was no time to give advice on business improvement. Also, when collecting complaints within the factory, there was no interface that allowed employees to easily report their complaints, which meant that it was difficult to obtain sufficient feedback for business improvement.

[0223] The specific processing 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to give advice on business improvement, means for providing an interface for inputting and sending complaints related to specific business operations, and means for sending complaints to the server. This provides an environment in which employees can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[0224] "Means 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] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints about work when no suggestions for improving work are given from employees.

[0226] The "means for collecting complaints as a countermeasure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints about business operations from employees when there is insufficient time to give advice on business improvement.

[0227] "Means for providing an interface for inputting and transmitting complaints related to specific work" means a method or apparatus that provides a user interface for employees to input complaints related to specific work and transmit them to the system.

[0228] The "means for transmitting complaints to a server" refers to a method or device for transmitting complaints entered by employees to a server.

[0229] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints about work improvements. Specifically, we will build a system that allows factory workers to easily report their work-related complaints through a robot.

[0230] System Program

[0231] The system uses Python's Tkinter library to create a GUI, which is installed on factory robots equipped with touchscreens, providing an interface for employees to input and submit complaints.

[0232] Program processing explanation

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

[0234] Hardware: Factory robots with touchscreens

[0235] Software: Python, Tkinter library

[0236] The server takes the complaints entered by the employee from the text box and sends them to the server. The specific process is as follows:

[0237] 1. The employee uses the touchscreen to enter their complaint.

[0238] 2. The complaint you entered will appear in the text box.

[0239] 3. Click the submit button and your complaint will be sent to the server.

[0240] 4. The server stores the received complaint in a database and notifies the administrator.

[0241] Specific examples

[0242] If a factory worker finds a new machine too complicated to operate and is inefficient, he or she can use the robot's touchscreen to register their complaints, like this:

[0243] "The new machine is complicated to operate and work efficiency is poor."

[0244] The employee clicks the submit button and sends the complaint to the system, which then passes it on to management to consider remedial measures.

[0245] Prompt Sentence Examples

[0246] "Create an application that provides an interface to collect complaints about work efficiency in a factory. Users can enter their complaints and send them to the system by clicking a submit button."

[0247] In this way, it is possible to provide an environment where employees can easily report their dissatisfaction and efficiently collect feedback for business improvement.

[0248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0249] Step 1:

[0250] The user uses the touch screen to input their complaint.

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

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

[0253] What it does: The user uses the touchscreen keyboard to type their complaint, which is then displayed in a text box in real time.

[0254] Step 2:

[0255] The user clicks the submit button.

[0256] Input: The user clicks the submit button.

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

[0258] Specific behavior: A click event occurs when the user taps the submit button on a touchscreen.

[0259] Step 3:

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

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

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

[0263] What happens: The terminal reads the contents of the text box and stores the text data in a variable.

[0264] Step 4:

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

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

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

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

[0269] Step 5:

[0270] The server stores the received complaints in a database.

[0271] Input: The text of the complaint sent to the server.

[0272] Output: A record of the complaint stored in a database.

[0273] Specific behavior: The server analyzes the complaint received and stores it as a new record in the database.

[0274] Step 6:

[0275] The server notifies the administrator of the complaint.

[0276] Input: Complaint records stored in the database.

[0277] Output: The notification message sent to the administrator.

[0278] Specific operation: Based on the complaints stored in the database, the server notifies the administrator of the complaints via email or notification system.

[0279] The above steps provide an environment where users can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[0280] Example 3

[0281] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0282] Conventional business improvement systems lack the means to respond when no suggestions for business improvement are made or when there is no time to provide advice for business improvement. Another issue is that it is difficult to efficiently collect and analyze user complaints and provide specific advice for business improvement. This limits the effectiveness of business improvement and makes it difficult to increase user satisfaction.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0284] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for a user to input complaints, means for a terminal to send the input 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 sentences to a generative AI model and generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[0285] The "means for collecting opinions on business improvement" is a function that provides an interface for collecting improvement proposals related to business from users.

[0286] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function that provides an interface for collecting complaints about business operations instead of users submitting suggestions for business improvement.

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

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

[0289] The "means for transmitting the complaints entered by the terminal to the server" is a communication function for transmitting the complaints entered by the user to the server via the network.

[0290] The "means for the server to receive complaints and store them in a database" is a function for the server to receive complaints sent by users and record them in a database.

[0291] The "means for the server to analyze the complaints stored" is a function for analyzing the complaint data stored in the database and extracting specific patterns or trends.

[0292] "Means for the server to send prompt sentences to the generative AI model and generate advice for business improvement" refers to a function that enables the server to send prompt sentences to the generative AI model based on the analysis results, and the generative AI model to generate advice for business improvement.

[0293] MODE FOR CARRYING OUT THE INVENTION

[0294] The present invention is a system for efficiently collecting and analyzing opinions and complaints about business improvement and providing specific advice on business improvement. A specific embodiment of this system will be described below.

[0295] System configuration

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

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

[0298] 2. Terminal: A device used to send data entered by a user to a server. Typically, a PC or smartphone is used.

[0299] 3. Server: Receives data sent by users, stores it in a database, and performs analysis. Uses Flask framework to process HTTP requests and Python to analyze data.

[0300] 4. Database: Data storage for saving user submitted opinions and complaints. A relational database such as MySQL is used.

[0301] 5. Generative AI model: An artificial intelligence model that allows the server to send prompts based on the analysis results and generate advice for business improvement.

[0302] Hardware and Software Details

[0303] User Interface: HTML, CSS, JavaScript

[0304] Devices: PC, smartphone

[0305] Server: Flask framework, Python

[0306] Database: MySQL

[0307] Generative AI model: A specific generative AI model (e.g., GPT-3 (registered trademark))

[0308] Specific examples

[0309] Consider the case where a user wants to express their dissatisfaction by saying, "The meeting is too long." The user enters "The meeting is too long" into the text box on the web browser and clicks the send button. This action sends the user's dissatisfaction from the terminal to the server.

[0310] The server receives HTTP POST requests using the Flask framework, parses the received data, and stores it in a MySQL database, for example by executing the following SQL query: INSERT INTO complaints (text, timestamp) VALUES ('Meetings take too long', '2023-10-01 12:00:00').

[0311] The server then uses Python's pandas library to read the complaints data from the database and perform analysis, for example, calculating the frequency of certain keywords to understand which types of complaints are most common.

[0312] Finally, the server sends a prompt to the generative AI model based on the analysis results. For example, in response to a complaint about "meetings being too long," the server sends a prompt such as "We received a complaint that the meetings were too long. Please generate business improvement advice for this complaint." to the generative AI model. The generative AI model generates business improvement advice based on this prompt and returns it to the server. The server 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 improving business operations. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] The user enters a complaint.

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

[0317] Step 2:

[0318] The terminal transmits the inputted 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 complaint text entered by the user. The input data is the complaint text 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 stores it in a database.

[0322] The server receives HTTP POST requests using the Flask framework. It parses the received complaint text and stores it in a MySQL database by adding a new record to a database table, recording the complaint text and a timestamp. The input data is the complaint text included in the HTTP POST request. The output data is the new record stored in the database.

[0323] Step 4:

[0324] The server analyzes the stored complaints.

[0325] The server periodically analyzes the complaints stored in the database. The analysis is performed using the Python pandas library. For example, the frequency of occurrence of specific keywords is calculated to determine which types of complaints are most common. The input data is the text data of complaints obtained from the database. The output data is the analysis results, including the frequency of occurrence of keywords and trends in complaints.

[0326] Step 5:

[0327] The server sends prompts to the generative AI model, which generates advice for business improvement.

[0328] The server sends a prompt to the generative AI model based on the analysis results. The prompt includes specific details of the complaint. The generative AI model generates advice for business improvement based on the received prompt and returns it to the server. The server provides this advice to the user. The input data are the analysis results and the generated prompt. The output data is the advice for business improvement returned by the generative AI model.

[0329] (Application example 3)

[0330] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0331] With conventional business improvement systems, it was difficult to efficiently collect and analyze opinions and complaints from employees. Furthermore, even when there was no time to provide advice on business improvement, there was a lack of means to appropriately handle employee complaints. This meant that feedback for business improvement was not fully utilized, hindering improvements in business efficiency.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 saving the input complaints in a database, means for inputting the saved complaints into a generative AI model and generating analysis results, and means for notifying a manager of the analysis results. This makes it possible to efficiently collect complaints from employees and analyze them using the generative AI model, thereby providing quick feedback to the manager and enabling them to take concrete action to improve business operations.

[0334] A "means for collecting opinions on business improvement" is a method or device for collecting opinions on business improvement from employees.

[0335] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints when no suggestions for improving work are given from employees.

[0336] The "means for collecting complaints as a measure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to give advice on business improvement.

[0337] The "means for providing an interface for inputting complaints" refers to a method or device that provides a user interface for employees to input complaints.

[0338] The "means for storing input complaints in a database" refers to a method or device for storing complaints input by employees in a database.

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

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

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

[0342] Hardware and software used

[0343] Hardware: Factory robots, touchscreen displays

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

[0345] System configuration and operation

[0346] 1. Display of complaint input interface:

[0347] The server runs a web server using Flask and serves a web page on a touchscreen display with a text box and a submit button, allowing employees to enter their complaints using this interface.

[0348] 2. Complaint Data Collection:

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

[0350] 3. Dissatisfaction analysis:

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

[0352] 4. Reporting to the Administrator:

[0353] The server notifies the administrator of the generated analysis results by email, using the Python smtplib library to send the email.

[0354] Specific examples

[0355] For example, a factory worker types "machine maintenance is overdue" and clicks 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 complaints: Machine maintenance is delayed

[0357] Prompt generative AI model: Generate improvement suggestions for this complaint.

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

[0359] In this way, employee complaints can be efficiently collected and analyzed using generative AI models, providing quick feedback to managers and enabling them to take concrete actions to improve operations.

[0360] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0361] Step 1:

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

[0363] Step 2:

[0364] The server receives the complaint data entered when the user clicks the submit button. The input is the complaint data sent by the user, and the output is to save the 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. As input, the complaint data stored in the database is used, and as output, the retrieved complaint data is input to the generative AI model. Specifically, the server executes an SQL query to retrieve complaint data from the database.

[0367] Step 4:

[0368] The server inputs the acquired dissatisfaction data into the generative AI model to generate analysis results. The inputs are the acquired dissatisfaction data and prompt sentences, 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 dissatisfaction data as prompt sentences.

[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 to be sent to the administrator. Specifically, the server uses Python's smtplib library to send an email containing the analysis results to the administrator.

[0371] In this way, complaints from employees can be efficiently collected, analyzed using a generative AI model, and the results communicated to managers, enabling them to take concrete action to improve operations.

[0372] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0373] "Example 1"

[0374] In one embodiment of the present invention, an emotion engine is incorporated. 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 components 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 proposing business improvements.

[0375] "Example 2"

[0376] 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 changes in the user's emotions from those inputs. For example, the emotion engine tracks changes in emotions, such as when a user feels dissatisfied when inputting their opinion for the first time, but after inputting several times, they start to feel satisfied. The results of tracking changes in emotions are used as reference information when updating proposals for business improvement. "Example 3"

[0377] Furthermore, the emotion engine takes the user's emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement proposals based on the results of analyzing the user's emotions and tracking changes in emotions. For example, for a task that the user is dissatisfied with, the emotion engine makes proposals to resolve that dissatisfaction. Also, for a task that the user is satisfied with, the emotion engine makes proposals to maintain that satisfaction. In this way, the emotion engine makes it possible to propose business improvements that take the user's emotions into consideration.

[0378] The processing flow of each embodiment will be described below.

[0379] "Example 1"

[0380] Step 1: The user inputs their ideas for business improvement.

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

[0382] Step 3: Make business improvement proposals based on the analysis results.

[0383] "Example 2"

[0384] Step 1: The user enters their opinion or complaint multiple times.

[0385] Step 2: The emotion engine tracks the emotional changes from the user's input.

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

[0387] "Example 3"

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

[0389] Step 2: For tasks that users are dissatisfied with, make suggestions to resolve the dissatisfaction.

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

[0391] Example 1

[0392] Next, a description will be given of 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."

[0393] Conventional business improvement systems lack the means to effectively collect and analyze user opinions and complaints, which means that business improvement proposals are not made appropriately. In addition, because analysis does not take into account user emotions, it is difficult to propose improvements that reflect the user's true intentions and emotions.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0395] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for sending the received opinions to a sentiment analysis engine, means for the sentiment analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for proposing business improvement based on the analysis results. This makes it possible to effectively collect user opinions and complaints and, through sentiment analysis, to propose business improvement that reflects the user's true intentions and emotions.

[0396] The "means for collecting opinions on business improvement" is a function that provides an interface for users to input improvement suggestions for business operations and send them to the system.

[0397] The "means for collecting complaints" is a function that provides an interface for users to input complaints or problems about work and send them to the system.

[0398] The "means for receiving opinions input by users" is a function by which the server receives opinions sent by users via the web interface.

[0399] The "means for transmitting received opinions to a sentiment analysis engine" is a function for transmitting user opinions received by the server to a sentiment analysis engine.

[0400] "Means for the emotion analysis engine to analyze opinions and extract emotional elements" is a function in which the emotion analysis engine analyzes user opinions and extracts emotional elements from them.

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

[0402] "Means for proposing business improvements based on analysis results" is a function that generates proposals for business improvements based on saved analysis results and notifies managers and related parties.

[0403] MODE FOR CARRYING OUT THE INVENTION

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

[0405] System configuration

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

[0407] Hardware and software used

[0408] The server uses a general web server and implements a sentiment analysis engine using NLTK, a Python natural language processing library, and TextBlob, a sentiment analysis library. The database uses MySQL and the web framework Flask. The terminal uses a web browser (e.g., GOOGLE CHROME (registered trademark)) for user access.

[0409] System Operation

[0410] A user accesses the system's web interface using a web browser. The user enters their opinion in a text box for entering opinions or complaints about business improvement and clicks the "Submit" button. For example, the user may enter "This task takes too long."

[0411] The server receives the opinions sent by the user as an HTTP request. The server analyzes this request and extracts the text data of the opinions. The extracted text data of the opinions is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinions 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 by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The server then makes business improvement proposals based on the stored analysis results. For example, if multiple users have similar complaints, the server generates proposals for reviewing the business process and notifies the administrator.

[0413] Examples of concrete examples and prompts

[0414] For example, a user accesses a web interface and types, "This task takes too long." The server receives this comment and sends it to a sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" or "stress" and returns the results to the server. The server stores the analysis results in a database and uses them later to make suggestions for improving the task.

[0415] Example prompt sentence:

[0416] "A user has entered an idea for improving business operations. Please use the sentiment engine to analyze the sentiment of this idea and save the results."

[0417] In this way, a system is realized in which the server, terminals, and users each play their respective roles, effectively collecting and analyzing opinions for business improvement.

[0418] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0419] Step 1:

[0420] User enters their opinion

[0421] A user accesses the system's web interface using a web browser. The user enters their opinion or complaint about business improvement in the text box and clicks 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 opinion

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

[0425] Step 3:

[0426] The server sends the opinion to the sentiment analysis engine

[0427] The server sends the extracted text data of opinions to a 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 text data of opinions, and the output is the data sent to the sentiment analysis engine.

[0428] Step 4:

[0429] Sentiment analysis engine analyzes opinions

[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 the opinion "This task takes too much time." The input data is the text data of the opinions, and the output is the results of the sentiment analysis.

[0431] Step 5:

[0432] The server stores the analysis results

[0433] The server receives the analysis results returned by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The input data is the sentiment analysis results, and the output is the data stored in the database.

[0434] Step 6:

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

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

[0437] (Application example 1)

[0438] Next, a description will be given of Application Example 1 of Embodiment 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] Conventional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. They also lacked the functionality to analyze the emotional components of collected opinions and complaints and provide feedback to managers in real time, making it difficult to quickly improve business operations.

[0440] The identification process by the identification 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of the 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] The "means for collecting opinions on work improvement" is a function that provides an interface for workers to input improvement suggestions regarding work.

[0442] The "means of collecting complaints as a countermeasure when no suggestions for work improvement are made" is a function that provides an interface for workers to input complaints about their work.

[0443] The "means of collecting complaints as a countermeasure when there is not enough time to give advice on business improvement" is a function that provides an interface for collecting complaints from workers when there is not enough time to give advice on business improvement.

[0444] "Means for analyzing the emotional components of opinions entered using an emotion analysis engine" means software or algorithms for analyzing the emotional components of opinions or complaints entered by workers.

[0445] "Means for notifying analysis results in real time" is a function for immediately notifying factory managers of the results of analysis by the emotion analysis engine.

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

[0447] As an embodiment of the present invention, a system is provided that collects opinions on work improvement and complaints from workers in a factory, analyzes the 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] Device: A smartphone or tablet where workers can input their opinions and complaints.

[0452] Network: The Internet or local network for connecting the server and devices.

[0453] software

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

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

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

[0457] Processing flow

[0458] 1. Feedback collection: Workers use their smartphones or tablets to access a web-based interface and enter their feedback and complaints about work improvements.

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

[0460] 3. Sentiment analysis: The sentiment analysis engine on the server analyzes the emotional components 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: The analysis results, as well as the entered opinions and complaints, are stored in a database.

[0463] Specific examples

[0464] For example, if a worker inputs an opinion such as, "This machine breaks down frequently, causing work delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." The results of this analysis are immediately notified to the factory manager and saved in a database.

[0465] Prompt Sentence Examples

[0466] Analyze the opinions entered by workers using a sentiment analysis engine to extract emotional elements. For example, extract emotions such as "dissatisfaction" and "stress" from the opinion, "This machine breaks down frequently, causing work delays."

[0467] In this way, it is possible to efficiently collect opinions and complaints from workers, analyze their emotional factors, and provide feedback to managers in real time, which is expected to lead to rapid improvements in operations.

[0468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0469] Step 1:

[0470] Users access a web-based interface using a smartphone or tablet and enter their opinions on business improvements or complaints. The entered data is saved in text format on the device.

[0471] Step 2:

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

[0473] Step 3:

[0474] The server passes the received opinions and complaints to a sentiment analysis engine, which uses the Natural Language Toolkit (NLTK) to analyze the emotional components 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 sent via email or a dedicated manager dashboard.

[0477] Step 5:

[0478] The server stores the input opinions and complaints along with the analysis results in a database, including the text of the opinion or complaint, its sentiment score, and a timestamp.

[0479] Step 6:

[0480] Based on the analysis results, factory managers can consider specific measures to improve operations. Managers can also access the database to view the history of past opinions and complaints.

[0481] In this way, it is possible to efficiently collect opinions and complaints from workers, analyze their emotional factors, and provide feedback to managers in real time, which is expected to lead to rapid improvements in operations.

[0482] Example 2

[0483] Next, a description will be given of 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 will be referred to as a "terminal."

[0484] Conventional business improvement systems lack the means to effectively collect and analyze user opinions and complaints, which can lead to problems with inappropriate business improvement proposals. Furthermore, the lack of a function to track changes in user emotions makes it difficult to accurately evaluate the effectiveness of business improvement. This often results in business improvement proposals that are not in line with users' actual feelings or needs.

[0485] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0486] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, emotion analysis means for analyzing opinions and complaints entered by users, means for tracking changes in user 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 by tracking changes in user emotions, more appropriate business improvement proposals can be made.

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

[0488] "Means for collecting complaints" refers to an interface or function that allows users to input complaints or problems related to work and send them to the system.

[0489] "Sentiment analysis means" refers to the technology or algorithms used to analyze text data of opinions and complaints entered by users and extract emotions from it.

[0490] "Means for tracking changes in emotions" refers to technologies and algorithms for tracking changes in a user's emotions based on opinions and complaints entered by the user multiple times.

[0491] "Means for updating business improvement proposals" refers to functions and processes for appropriately updating the content of business improvement proposals based on data obtained from the sentiment analysis means and the means for tracking changes in sentiment.

[0492] The present invention is a system for effectively collecting and analyzing opinions and complaints about business improvement. A specific embodiment of this system will be 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: The device on which a user enters their opinion or complaint. This can include a computer, smartphone, or tablet.

[0497] Sentiment analysis engine: Software for analyzing user input data and extracting emotions. As a specific example, we use IBM Watson (registered trademark) Natural Language Understanding.

[0498] Program processing

[0499] Complaint collection interface

[0500] 1. User enters complaint:

[0501] The user enters a complaint related to a particular job into a text box on the terminal.

[0502] For example, type "The meeting is too long."

[0503] 2. User submits complaint:

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

[0505] 3. The server receives the complaint:

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

[0507] Tracking emotions with the emotion engine

[0508] 1. The server passes the user's input to the emotion 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 parse 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 "dissatisfied."

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

[0514] The server passes the user's input data over multiple times to the emotion engine and tracks changes in emotion.

[0515] Example: Tracking the change in emotion from "dissatisfied" on the first entry to "satisfied" after a few entries.

[0516] 4. The server reflects changes in emotions in business improvement proposals:

[0517] The server updates its business improvement proposals based on data on changes in emotions obtained from the emotion engine.

[0518] Example: Reflect the feedback that "the satisfaction has increased because the meeting time has been shortened" in proposals for operational improvement.

[0519] Examples and prompts

[0520] Specific examples

[0521] User input: "Meeting is too long"

[0522] Emotion engine analysis results: "Dissatisfied" after the first input, "Satisfied" after a few inputs

[0523] Suggestion for business improvement: "By shortening meeting times, user satisfaction will improve."

[0524] Prompt Sentence Examples

[0525] "Enter a specific work-related complaint. Example: 'Meetings are too long.'"

[0526] "Please tell us how your feelings have changed regarding recent improvements in your work. Example: 'I'm happy that meeting times have been reduced.'"

[0527] This system collects user complaints and uses an emotion engine to track changes in emotions, thereby achieving more effective business improvements.

[0528] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0529] Step 1:

[0530] User enters complaint

[0531] Input: The user enters a specific job-related complaint into a text box on the terminal.

[0532] What happens: The user types "meetings are too long" into a text box on their browser.

[0533] Output: Generates text data of the input complaint.

[0534] Step 2:

[0535] User submits complaint

[0536] Input: The text data of the complaint entered by the user.

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

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

[0539] Step 3:

[0540] The server receives the complaint

[0541] Input: The text of the complaint submitted by the user.

[0542] What happens: The server receives the HTTP POST request and stores the complaint "meeting is too long" in a database.

[0543] Output: Complaint text data stored in a database.

[0544] Step 4:

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

[0546] Input: Complaint text data stored in a database.

[0547] What happens: The server sends an API request to the emotion engine to parse the text "The meeting is too long."

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

[0549] Step 5:

[0550] Emotion engine analyzes emotions

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

[0552] What it does: The emotion engine uses natural language processing techniques to analyze emotions from text.

[0553] Output: Sentiment analysis result of "Dissatisfied".

[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 "dissatisfaction" at the first input to "satisfaction" after several inputs.

[0558] Output: Emotion change data.

[0559] Step 7:

[0560] The server reflects changes in emotions in business improvement proposals

[0561] Input: Emotion change data.

[0562] Specific operation: The server reflects the feedback that "satisfaction has increased because the meeting time has been shortened" in its business improvement proposals.

[0563] Output: Updated business improvement proposals.

[0564] (Application example 2)

[0565] Next, a description will be given of 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 will be referred to as a "terminal."

[0566] Conventional business improvement systems have difficulty effectively collecting workers' opinions and complaints and tracking changes in their emotions. Furthermore, they are unable to generate specific improvement proposals based on the collected opinions and complaints, limiting the effectiveness of business improvement. In particular, they are unable to properly reflect the feedback of workers working in factories, resulting in delays in improving the work environment and machine operation.

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

[0568] In this invention, the server includes: means for receiving opinions or complaints about machine operation or the work environment input from each worker working in the factory; means for recognizing a score indicating the worker's emotion from the input opinions or complaints using an emotion engine; prompts for generating improvement proposals for the machine or the work environment based on the input opinions or complaints and the scores of each worker; and means for generating improvement proposals for the machine or the work environment using a generative AI. The server further includes means for tracking changes in the scores of each worker, and the improvement proposal generating means generates the improvement proposals for the machine or the work environment based on the input opinions or complaints and the changes in the scores of each worker using the generative AI. This effectively collects worker feedback and tracks changes in emotion, thereby generating specific improvement proposals and enabling rapid and effective business improvement. The server may further include means that is installed on a machine in a factory and provides an interface for the worker to input the opinion or complaint.

[0569] "Means for collecting opinions on business improvement" refers to methods or devices for collecting suggestions and opinions on improving the efficiency and quality of business from workers and related parties.

[0570] A "means for collecting complaints" is a method or device for collecting complaints and problems that workers and other related parties have about their work or work environment.

[0571] "Means for providing an interface that allows workers to input their complaints and opinions about machine operation and the work environment" refers to a method or device that provides a user interface that allows workers to input their own opinions and complaints and send them to the 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] The "means for generating improvement proposals for machine operation and working environment based on changes in emotions" refers to a method or device for generating specific improvement proposals 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 a worker, and an emotion engine. The server includes means for collecting opinions on business improvement, means for collecting complaints, means for providing an interface through which workers can input their complaints and opinions about machine operation and the work environment, the emotion engine, and means for generating improvement proposals for machine operation and the work environment based on changes in emotion.

[0575] Hardware and software 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 calculation

[0579] The server receives the feedback text entered by the worker through the terminal. The feedback text is subjected to sentiment analysis using TextBlob to calculate a positive or negative score. This sentiment score is saved in JSON format along with the feedback data. The server tracks the changes in the sentiment of a specific worker and generates specific improvement suggestions based on this.

[0580] Specific examples

[0581] For example, if a worker enters feedback such as "The robot's movements are slow," TextBlob analyzes this text and calculates a negative emotion score. Next, if the worker enters feedback such as "The robot's movements have improved recently," a positive emotion score is calculated. The server tracks these emotion scores and generates suggestions for improving the robot's movements and working environment based on the changes in emotion.

[0582] Prompt Sentence Examples

[0583] User ID: user123

[0584] Feedback: The robot is slow.

[0585] Sentiment score: -0.5

[0586] User ID: user123

[0587] Feedback: Recently, the robot's behavior has improved.

[0588] Sentiment score: 0.8

[0589] In this way, the feedback system effectively collects workers' opinions and complaints, tracks changes in their emotions, and generates specific improvement suggestions, enabling rapid and effective improvements to operations.

[0590] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0591] Step 1:

[0592] A user who is a worker working in a factory uses a terminal to input feedback expressing opinions or dissatisfaction with the operation of a machine or the working environment.

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

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

[0595] Specific action: The user types "The robot is slow" and clicks the submit button. It may also be installed on machines in a factory to provide an interface for workers to input their opinions or complaints.

[0596] Step 2:

[0597] The server receives the feedback text.

[0598] Input: The feedback text submitted by the user.

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

[0600] Specific behavior: The server receives the feedback text "The robot is slow" and stores it in the database.

[0601] Step 3:

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

[0603] Input: The saved feedback text.

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

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

[0606] Step 4:

[0607] The server stores the emotion scores along with the feedback data in JSON format.

[0608] Input: Sentiment score and feedback text.

[0609] Output: Feedback data in JSON format.

[0610] Specific operation: The server saves the data "User ID: user123, Feedback: Robot is slow, Emotion score: -0.5" in JSON format.

[0611] Step 5:

[0612] The server tracks the emotional changes of a particular user.

[0613] Input: Stored feedback data.

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

[0615] What happens: The server analyzes the past feedback data of "user123" and tracks changes in the emotion score.

[0616] Step 6:

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

[0618] Input: User emotion change data.

[0619] Output: Improvement suggestions.

[0620] Specific operation: The server generates improvement proposals for the machine or the work environment based on the feedback data representing the input opinions or dissatisfaction and the changes in each worker's score, using a prompt sentence instructing the server to generate improvement proposals for the machine or the work environment and a generative AI. For example, the server generates a suggestion to improve the robot's movement speed based on the change in the emotion score of "user123" from negative to positive. Example prompt sentence: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5, User ID: user123, Feedback: The robot's behavior has been improved recently., Emotion score: 0.8 Based on this feedback data and changes in sentiment scores, generate improvement recommendations for machinery or the working environment within the factory.

[0621] Example 3

[0622] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0623] Conventional business improvement systems do not provide suggestions that fully consider users' dissatisfaction and feelings, limiting the effectiveness of business improvement. Furthermore, there are insufficient measures in place for cases where no suggestions for business improvement are made or there is no time to provide advice. This leaves users dissatisfied and prevents business efficiency from improving.

[0624] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

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

[0626] The "means for collecting opinions on business improvement" is a means for providing an interface that enables users to input improvement suggestions related to business operations and send them to the system.

[0627] The "means for collecting complaints as a measure when no suggestions for business improvement are given" is a means for providing an interface that allows users to input complaints about business and send them to the system.

[0628] The "means for collecting complaints as a measure when there is no time to give advice on business improvement" is a means for providing an interface that allows users to input complaints about business and send them to the system.

[0629] The "emotion analysis means for analyzing dissatisfaction input by the user" is a means for analyzing text data input by the user and determining the emotion.

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

[0631] The "means for returning the generated proposal to the user" is a means for notifying the user of the business improvement proposal generated by the generative model means.

[0632] The present invention is a system for collecting users' work-related complaints, performing emotion analysis, and generating business improvement proposals based on the results. Specific embodiments of this system are described below.

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

[0634] The device sends the user's complaints to the server using an AJAX request, and the server stores the complaints in a MySQL database using the Python Flask framework to connect to the database and execute an INSERT query.

[0635] The server passes the saved dissatisfaction data to the sentiment analysis means. The sentiment analysis means is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The sentiment analysis means analyzes the text data entered by the user and determines their sentiment.

[0636] The sentiment analysis means passes data to the generative model means, which generates business improvement proposals based on the analysis results. The generative model means generates proposals using a generative AI model. Specifically, a prompt sentence is input into a generative AI model such as GPT-3, and a proposal is generated. For example, if a user inputs "writing daily reports is a hassle," the following prompt sentence is input into the generative AI model:

[0637] A user has entered "It's a hassle to create reports every day." Please provide suggestions for business improvements to resolve this issue.

[0638] The generative AI model suggests "introducing an automated report creation tool."

[0639] The server returns the generated suggestions to the user. Specifically, the suggestions are returned as a response to the AJAX request, and the terminal displays them to the user. The user can then check the suggestions on their browser and take specific actions to improve their work.

[0640] In this way, the system can propose improvements to the business taking into account the user's feelings, effectively alleviating the user's dissatisfaction and improving business efficiency. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] The user enters a complaint.

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

[0644] Step 2:

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

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

[0647] Step 3:

[0648] The server stores the complaint in a database.

[0649] The server stores the received complaint data in a MySQL database. Specifically, it uses the Python Flask framework to connect to the database and execute INSERT queries. 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 means.

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

[0653] Step 5:

[0654] A sentiment analysis tool analyzes dissatisfaction.

[0655] The sentiment analysis means 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 it as "dissatisfied." The input data is the dissatisfaction data passed to the sentiment analysis means. The output is the analyzed sentiment data.

[0656] Step 6:

[0657] The sentiment analysis means passes the data to a generative model means that generates business improvement suggestions.

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

[0659] Step 7:

[0660] A generative model means generates business improvement proposals.

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

[0662] A user has entered "It's a hassle to create reports every day." Please provide suggestions for business improvements to resolve this issue.

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

[0664] Step 8:

[0665] The server returns the suggestions to the user.

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

[0667] In this way, the system can propose work improvement measures that take the user's feelings into consideration, effectively alleviating the user's dissatisfaction and improving work efficiency.

[0668] (Application example 3)

[0669] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0670] With conventional business improvement systems, it was difficult to efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate business improvements. Furthermore, when a worker was feeling dissatisfied, there was a lack of a way to accurately grasp their emotions and quickly respond. This led to problems such as a decline in work efficiency and a drop in worker motivation.

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

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

[0673] "Means for collecting opinions on business improvement" refers to an interface or method for collecting improvement suggestions for business operations from workers and users.

[0674] "Means for collecting complaints as a countermeasure when no suggestions for business improvement are received" refers to an interface or method for collecting complaints from workers or users when no suggestions for business improvement are received.

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

[0676] A "voice or text complaint collection means" is an interface or method that allows a worker or user to input a complaint by voice or text and transmit it to the system.

[0677] The "means for analyzing emotions from collected complaints" refers to an algorithm or method for analyzing the content of collected complaints and evaluating the emotions of workers or users.

[0678] The "means for proposing business improvement based on the results of sentiment analysis" refers to an algorithm or method for generating appropriate business improvement proposals based on the results of sentiment analysis and providing them to workers or users.

[0679] To implement this invention, the following system configuration is required: The system includes means for collecting opinions on business improvement, means for collecting complaints, means for collecting complaints by voice input or text input, means for emotionally analyzing the collected complaints, and means for proposing business improvement based on the results of the emotional analysis.

[0680] Hardware and software used

[0681] Hardware

[0682] Microphone: Used to collect voice input from the worker or user.

[0683] Factory robot: Acts as an interface to collect complaints and opinions from workers and make suggestions for improving operations.

[0684] software

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

[0686] speech_recognition library: Used to convert voice input to text.

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

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

[0689] Data processing and calculation

[0690] Voice Recognition

[0691] The server collects the voice of the worker or user through a microphone and converts the voice into text using the speech_recognition library, which allows the voice input to be captured in 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 to analyze the sentiment, and outputs the analysis results as a sentiment label: positive, negative, or neutral.

[0694] Business improvement proposals

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

[0696] Specific examples

[0697] When a worker says to the robot, "This machine is difficult to operate," the robot collects the voice and sends it to a server. The server converts the voice into text and performs emotional analysis. If the analysis result is "negative," the robot will suggest, "We suggest revising the work procedures to alleviate your dissatisfaction with this task."

[0698] Prompt Sentence Examples

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

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

[0701] Robot: Suggestion: To resolve the dissatisfaction with this job, I suggest reviewing the work procedures.

[0702] In this way, the system can efficiently collect complaints and opinions from workers, analyze their emotions, and make appropriate suggestions for improving operations.

[0703] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0704] Step 1:

[0705] The user vocalizes their complaint.

[0706] The user inputs their complaints by voice through a microphone, and the input voice data is sent from the microphone to the server.

[0707] Step 2:

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

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

[0710] Step 3:

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

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

[0713] Step 4:

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

[0715] The server generates appropriate business improvement proposals based on the results of emotion analysis. For example, if negative emotions are detected, it makes specific proposals to alleviate the dissatisfaction. The input is emotion labels, and the output is business improvement proposals.

[0716] Step 5:

[0717] The server provides the user with suggestions for improving business operations.

[0718] The server provides the generated business improvement proposals to the user. Specifically, the proposals are displayed or read aloud through the robot's display or voice output. The input is the business improvement proposal, 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0720] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0721] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0722] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0723] [Second embodiment]

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

[0725] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0726] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure.

[0727] The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0728] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0729] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0730] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0731] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0732] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0733] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0734] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0735] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0737] "Example 1"

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

[0739] "Example 2"

[0740] Furthermore, as a countermeasure when no suggestions for business improvement are received, an interface for collecting complaints related to specific business operations is also provided. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[0741] "Example 3"

[0742] Furthermore, as a countermeasure for cases where there is no time to provide advice on business improvement, an interface is also provided for collecting complaints related to specific business operations. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[0743] The processing flow of each embodiment will be described below.

[0744] "Example 1"

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

[0746] Step 2: The user enters their opinion related to a specific task.

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

[0748] "Example 2"

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

[0750] Step 2: The user enters a specific job-related complaint.

[0751] Step 3: The user submits their complaint to the system.

[0752] "Example 3"

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

[0754] Step 2: The user enters a specific job-related complaint.

[0755] Step 3: The user submits their complaint to the system.

[0756] Example 1

[0757] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0758] Conventional business improvement systems have difficulty efficiently collecting user opinions and complaints and proposing appropriate improvement measures. Furthermore, if managers do not have time to provide advice on business improvement, users' dissatisfaction accumulates, resulting in a decline in business efficiency. Furthermore, there is a lack of means to effectively manage the collected opinions and complaints and derive appropriate improvement measures.

[0759] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0760] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting 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 send opinions, means for saving the input opinions in a database, means for displaying the saved opinions on an administration screen, and means for an administrator to input prompt sentences to the generative AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[0761] The "means for collecting opinions on business improvement" is a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business operations.

[0762] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function for collecting complaints and problems instead of users when they do not submit suggestions for business improvement.

[0763] The "means of collecting complaints as a measure when there is no time to give advice on business improvement" is a function for collecting complaints and problems from users when the administrator does not have time to give advice on business improvement.

[0764] A "means for providing a web-based interface" is a function for providing web pages and applications that can be accessed by users via the Internet.

[0765] "Means for users to input and send opinions" is a function that allows users to input their own opinions and suggestions and send them to the system.

[0766] The "means for saving input opinions in a database" is a function for recording and saving opinions and suggestions sent by users in a database.

[0767] The "means for displaying saved opinions on the management screen" is a function for displaying the opinions and suggestions saved in the database on the management screen so that the administrator can check them.

[0768] "A means for an administrator to input prompt statements into a generative AI model and obtain suggestions" is a function that allows an administrator to input specific questions or instructions into a generative AI model and obtain suggestions or answers from the AI ​​based on those questions or instructions.

[0769] MODE FOR CARRYING OUT THE INVENTION

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

[0771] System configuration

[0772] server

[0773] The server uses web server software such as Apache or Nginx to provide a web-based interface, and has the functionality to receive opinions submitted by users and store them in a database, using a database management system such as MySQL or PostgreSQL.

[0774] Terminal

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

[0776] User

[0777] Users use the system to provide suggestions for business improvement. Users access the web interface, enter their suggestions in the suggestion input form, and submit their suggestions by pressing the send button.

[0778] Program processing

[0779] The server provides a web page for users to access. This web page is created using HTML, CSS, and JavaScript. When a 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 that can be accessed by administrators. This administration screen is built using server-side scripts such as PHP and Python.

[0781] The administrator checks user feedback on the management screen and, if necessary, inputs prompts into the generative AI model, which then suggests appropriate improvements and next actions based on the input prompts.

[0782] Specific examples

[0783] For example, consider a user who wants to input an opinion such as "Please consider introducing a new project management tool." The user accesses a web interface, enters this information into the opinion input form, and presses the submit button. The server receives this data and stores it in a database.

[0784] The administrator confirms this opinion in the management screen and enters the following prompt for the generative AI model:

[0785] A user has submitted a request to "consider introducing a new project management tool." How should we handle this request and what improvements should we suggest?

[0786] The generative AI model then suggests appropriate improvements based on the prompt, such as, "Task management efficiency could be improved by introducing a new project management tool. Specifically, consider using a task management tool."

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

[0788] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0789] Step 1:

[0790] The user accesses the web interface.

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

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

[0793] Output: A web page is displayed.

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

[0795] Step 2:

[0796] The user enters their opinion in the opinion input form and presses the send button.

[0797] Specific behavior: The user uses the keyboard to type in their opinion and then uses the mouse to click the submit button.

[0798] Input: User-entered opinion.

[0799] Output: Submit button click event.

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

[0801] Step 3:

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

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

[0804] Input: Opinion data entered by the user.

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

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

[0807] Step 4:

[0808] The server stores the received data in a database.

[0809] How it works: The server receives data using Python's Flask framework and stores it in a database using SQLAlchemy.

[0810] Input: Opinion data included in the body of the 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 saved opinions on the administration screen.

[0815] Specific operation: The server generates an administration screen using an HTML template engine and displays the saved opinions.

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

[0817] Output: HTML page of the admin panel.

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

[0819] Step 6:

[0820] The administrator checks the opinions on the management screen and, if necessary, inputs prompt text into the generative AI model.

[0821] Specific operation: The administrator enters a prompt text in the text box on the administration screen and clicks the send button.

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

[0823] Output: The request to the generative AI model.

[0824] Data processing / calculation: Converting the prompt text entered by the administrator into an appropriate format for sending to the generative AI model.

[0825] Step 7:

[0826] A generative AI model returns suggestions.

[0827] Specific operation: The generative 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 / computation: The generative AI model analyzes the prompt and generates text that suggests appropriate improvements or next actions.

[0831] (Application example 1)

[0832] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0833] The previous system for collecting opinions for business improvement limited the means by which employees could submit their opinions, making it difficult to do so, especially within the factory. Furthermore, there was a lack of a mechanism for efficiently sending opinions to a cloud-based database, making it difficult for managers to quickly review the opinions.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0835] In this invention, the server includes means for collecting suggestions for business improvement, means for collecting complaints as a countermeasure when no suggestions for business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice for business improvement, means that is installed on machines in the factory and provides an interface for employees to input suggestions for business improvement, and means for sending the suggestions input by employees to a cloud-based database. This enables employees to easily submit suggestions for business improvement in the factory, and the suggestions are quickly sent to the cloud-based database where managers can efficiently check them.

[0836] The "means for soliciting opinions on business improvement" is a system that provides an interface for employees to submit suggestions for improving or streamlining business operations.

[0837] A "grievance channel" is a system that provides an interface for employees to submit complaints or issues about their work.

[0838] "A means installed on machines in a factory that provides an interface for employees to input their opinions on business improvements" refers to a system that is installed on machines in a factory and includes input devices such as touch screens and keyboards that allow employees to directly input their opinions.

[0839] The "means of transmitting opinions entered by employees to a cloud-based database" refers to a system that transmits opinions entered by employees to a cloud-based database via the Internet, allowing managers to check them later.

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

[0841] A system for implementing the present invention includes a means for providing an interface installed on a machine in a factory and a means for transmitting opinions entered by employees to a cloud-based database. A specific embodiment of this system will be described below.

[0842] System configuration

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

[0844] 1. Interfaces installed on machines in the factory:

[0845] Hardware: Touchscreen displays, keyboards, factory robots

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

[0847] 2. Cloud-based database:

[0848] Hardware: Remote Server

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

[0850] Program processing

[0851] The server provides an interface for employees to input their suggestions for business improvement. Employees enter their suggestions using a touchscreen display or keyboard, and the suggestions are retrieved by a Python program. The retrieved suggestions are then sent to a cloud-based database as HTTP POST requests using the requests library.

[0852] Data processing and calculation

[0853] Data processing: The opinions entered by employees are obtained in text format. The entered text is converted into an appropriate format and prepared as the payload for the HTTP request.

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

[0855] Specific examples

[0856] For example, consider a scenario in which a factory worker types in their request, "I'd like a new sensor installed to adjust the speed of the production line," and clicks the submit button. In this case, the worker's request is passed to a Python program via a touchscreen display and then sent to a cloud-based database using the requests library.

[0857] Prompt Sentence Examples

[0858] Please enter your opinion such as "We would like a new sensor 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 the suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Step 1:

[0862] A user inputs their thoughts on business improvement using a touchscreen display or keyboard installed on a machine in a factory. The inputted thoughts are sent to a terminal in text format. The input data is the text of the thoughts input by the user, and the output data is the text data stored in the terminal.

[0863] Step 2:

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

[0865] Step 3:

[0866] The terminal uses the requests library to send an HTTP POST request to the 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 the opinion data sent to the cloud-based database and stores it in the database management system, where 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 the data through a database management system so that the administrator can later review the opinion data. The input data is the opinion data stored in the database, and the output data is the opinion data provided in a format accessible to the administrator.

[0871] In this way, users can easily submit suggestions for improving operations within the factory, and those suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[0872] Example 2

[0873] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0874] Conventional business improvement systems were inadequate when users did not provide feedback or when there was no time to provide advice on business improvement. Furthermore, there was a lack of a means to efficiently collect and analyze user complaints, making it difficult to implement specific measures for business improvement. This resulted in problems such as delays in business efficiency and improvements.

[0875] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0876] In this invention, the server includes a means for users to input complaints, a means for users to transmit complaints, a means for the server to receive the complaints, a means for the server to store the complaints in a database, and a means for the server to analyze the complaint data, thereby enabling efficient collection and analysis of complaints from users.

[0877] "Means for collecting opinions on business improvement" refers to interfaces and functions for collecting opinions on business improvement from users.

[0878] "Means for collecting complaints as a countermeasure when no suggestions for business improvement are made" refers to interfaces and functions for collecting business-related complaints from users.

[0879] "Means for users to input complaints" refers to a text box or input field where users can enter their complaints in text form.

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

[0881] "Means by which the server receives complaints" refers to the functions and processes by which the server receives complaints sent by users.

[0882] "Means by which the server stores complaints in a database" refers to the functionality or process by which received complaints are stored in a database.

[0883] "Means by which the server analyzes the complaint data" refers to the functions and processes for analyzing the complaint data stored in the database and extracting trends and frequently occurring keywords.

[0884] This invention is a system for collecting and analyzing complaints related to a specific task when no suggestions for improving the task are made. A specific embodiment of this system will be described below.

[0885] Hardware and software used

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

[0887] Software: Web browser, Web server (Apache, Nginx, etc.), Backend framework (Django, Flask, etc.), Database (MySQL, PostgreSQL, etc.)

[0888] System Overview

[0889] The system provides an interface for users to input work-related complaints and send them to a server, which stores the complaints in a database and periodically analyzes them.

[0890] User operations

[0891] Users access the system interface using a web browser, which displays a text box where users can enter their work-related complaints. Once they have completed the input, they click a submit button to send the complaint to the server.

[0892] Specific examples

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

[0894] Server Processing

[0895] The server receives the HTTP POST request and extracts the complaint text from the request body. This process is handled by a backend framework (e.g., Django or Flask). The extracted complaint text is stored in a database. Along with the complaint text, metadata such as the submission date and user ID are also stored in the database.

[0896] Analyzing the data

[0897] The server periodically analyzes the complaint data stored in the database, uses text mining technology to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[0898] Specific examples

[0899] The server outputs the analysis result that there are many complaints that "meetings are too long."

[0900] Example prompts for generative AI models

[0901] "Analyze work-related complaints entered by users and identify the most common types of complaints."

[0902] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[0903] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] User enters complaint

[0906] Specific behavior: A user opens a web browser and accesses the system interface, which displays a text box where the user can enter their 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] What happens: The user enters their complaint into the text box and then clicks the submit button, which sends the complaint 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] Server receives complaint

[0916] What happens: The server receives the HTTP POST request and extracts the complaint text from the request body. This is handled by the backend framework (e.g., Django, Flask).

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

[0918] Output: Extracted complaint text

[0919] Step 4:

[0920] The server stores the complaint in a database

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

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

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

[0924] Step 5:

[0925] The server analyzes the complaint data

[0926] How it works: The server periodically analyzes the complaint data stored in the database, uses text mining techniques to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[0927] Input: Complaint data stored in the database

[0928] Output: Dissatisfaction trends, frequently occurring keywords, and analysis results

[0929] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[0930] (Application example 2)

[0931] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0932] With conventional business improvement systems, it was difficult to efficiently collect opinions and complaints from employees, and there was a lack of means to properly collect complaints, especially when no suggestions for business improvement were made or when there was no time to give advice on business improvement. Also, when collecting complaints within the factory, there was no interface that allowed employees to easily report their complaints, which meant that it was difficult to obtain sufficient feedback for business improvement.

[0933] The specific processing 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to give advice on business improvement, means for providing an interface for inputting and sending complaints related to specific business operations, and means for sending complaints to the server. This provides an environment in which employees can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[0934] "Means 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] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints about work when no suggestions for improving work are given from employees.

[0936] The "means for collecting complaints as a countermeasure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints about business operations from employees when there is insufficient time to give advice on business improvement.

[0937] "Means for providing an interface for inputting and transmitting grievances related to specific work tasks" means a method or apparatus that provides a user interface for employees to input grievances related to specific work tasks and transmit them to the system.

[0938] The "means for transmitting complaints to a server" refers to a method or device for transmitting complaints entered by employees to a server.

[0939] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints about work improvements. Specifically, we will build a system that allows factory workers to easily report their work-related complaints through a robot.

[0940] System Program

[0941] The system uses Python's Tkinter library to create a GUI, which is installed on factory robots equipped with touchscreens, providing an interface for employees to input and submit complaints.

[0942] Program processing explanation

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

[0944] Hardware: Factory robots with touchscreens

[0945] Software: Python, Tkinter library

[0946] The server retrieves the complaints entered by the employee from the text box and sends them to the server. The specific process is as follows:

[0947] 1. The employee uses the touchscreen to enter their complaint.

[0948] 2. The complaint you entered will appear in the text box.

[0949] 3. Click the submit button and your complaint will be sent to the server.

[0950] 4. The server stores the received complaint in a database and notifies the administrator.

[0951] Specific examples

[0952] If a factory worker finds a new machine too complicated to operate and is inefficient, he or she can use the robot's touchscreen to register their complaints, like this:

[0953] "The new machine is complicated to operate and work efficiency is poor."

[0954] The employee clicks the submit button and sends the complaint to the system, which then passes it on to management to consider remedial measures.

[0955] Prompt Sentence Examples

[0956] "Create an application that provides an interface to collect complaints about work efficiency in a factory. Users can enter their complaints and send them to the system by clicking a submit button."

[0957] In this way, it is possible to provide an environment where employees can easily report their dissatisfaction and efficiently collect feedback for business improvement.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Step 1:

[0960] The user uses the touch screen to input their complaint.

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

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

[0963] What it does: The user uses the touchscreen keyboard to type their complaint, which is then displayed in a text box in real time.

[0964] Step 2:

[0965] The user clicks the submit button.

[0966] Input: The user clicks the submit button.

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

[0968] Specific behavior: A click event occurs when the user taps the submit button on a touchscreen.

[0969] Step 3:

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

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

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

[0973] What happens: The terminal reads the contents of the text box and stores the text data in a variable.

[0974] Step 4:

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

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

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

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

[0979] Step 5:

[0980] The server stores the received complaints in a database.

[0981] Input: The text of the complaint sent to the server.

[0982] Output: A record of the complaint stored in a database.

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

[0984] Step 6:

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

[0986] Input: Complaint records stored in the database.

[0987] Output: The notification message sent to the administrator.

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

[0989] The above steps provide an environment where users can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[0990] Example 3

[0991] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0992] Conventional business improvement systems lack the means to respond when no suggestions for business improvement are made or when there is no time to provide advice for business improvement. Another issue is that it is difficult to efficiently collect and analyze user complaints and provide specific advice for business improvement. This limits the effectiveness of business improvement and makes it difficult to increase user satisfaction.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0994] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for a user to input complaints, means for a terminal to send the input 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 sentences to a generative AI model and generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[0995] The "means for collecting opinions on business improvement" is a function that provides an interface for collecting improvement proposals related to business from users.

[0996] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function that provides an interface for collecting complaints about business operations instead of users submitting suggestions for business improvement.

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

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

[0999] The "means for transmitting the complaints entered by the terminal to the server" is a communication function for transmitting the complaints entered by the user to the server via the network.

[1000] The "means for the server to receive complaints and store them in a database" is a function for the server to receive complaints sent by users and record them in a database.

[1001] The "means for the server to analyze the complaints stored" is a function for analyzing the complaint data stored in the database and extracting specific patterns or trends.

[1002] "Means for the server to send prompt sentences to the generative AI model and generate advice for business improvement" refers to a function that enables the server to send prompt sentences to the generative AI model based on the analysis results, and the generative AI model to generate advice for business improvement.

[1003] MODE FOR CARRYING OUT THE INVENTION

[1004] The present invention is a system for efficiently collecting and analyzing opinions and complaints about business improvement and providing specific advice on business improvement. A specific embodiment of this system will be described below.

[1005] System configuration

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

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

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

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

[1010] 4. Database: Data storage for saving user submitted opinions and complaints. A relational database such as MySQL is used.

[1011] 5. Generative AI model: An artificial intelligence model that allows the server to send prompts based on the analysis results and generate advice for business improvement.

[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 examples

[1019] Consider the case where a user wants to express their dissatisfaction by saying, "The meeting is too long." The user enters "The meeting is too long" into the text box on the web browser and clicks the send button. This action sends the user's dissatisfaction from the terminal to the server.

[1020] The server receives HTTP POST requests using the Flask framework, parses the received data, and stores it in a MySQL database, for example by executing the following SQL query: INSERT INTO complaints (text, timestamp) VALUES ('Meetings take too long', '2023-10-01 12:00:00').

[1021] The server then uses Python's pandas library to read the complaints data from the database and perform analysis, for example, calculating the frequency of certain keywords to understand which types of complaints are most common.

[1022] Finally, the server sends a prompt to the generative AI model based on the analysis results. For example, in response to a complaint about "meetings being too long," the server sends a prompt such as "We received a complaint that the meetings were too long. Please generate business improvement advice for this complaint." to the generative AI model. The generative AI model generates business improvement advice based on this prompt and returns it to the server. The server 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 improving business operations. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1024] Step 1:

[1025] The user enters a complaint.

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

[1027] Step 2:

[1028] The terminal transmits the inputted 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 complaint text entered by the user. The input data is the complaint text 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 stores it in a database.

[1032] The server receives HTTP POST requests using the Flask framework. It parses the received complaint text and stores it in a MySQL database by adding a new record to a database table, recording the complaint text and a timestamp. The input data is the complaint text included in the HTTP POST request. The output data is the new record stored in the database.

[1033] Step 4:

[1034] The server analyzes the stored complaints.

[1035] The server periodically analyzes the complaints stored in the database. The analysis is performed using the Python pandas library. For example, the frequency of occurrence of specific keywords is calculated to determine which types of complaints are most common. The input data is the text data of complaints obtained from the database. The output data is the analysis results, including the frequency of occurrence of keywords and trends in complaints.

[1036] Step 5:

[1037] The server sends prompts to the generative AI model, which generates advice for business improvement.

[1038] The server sends a prompt to the generative AI model based on the analysis results. The prompt includes specific details of the complaint. The generative AI model generates advice for business improvement based on the received prompt and returns it to the server. The server provides this advice to the user. The input data are the analysis results and the generated prompt. The output data is the advice for business improvement returned by the generative AI model.

[1039] (Application example 3)

[1040] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1041] With conventional business improvement systems, it was difficult to efficiently collect and analyze opinions and complaints from employees. Furthermore, even when there was no time to provide advice on business improvement, there was a lack of means to appropriately handle employee complaints. This meant that feedback for business improvement was not fully utilized, hindering improvements in business efficiency.

[1042] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 saving the input complaints in a database, means for inputting the saved complaints into a generative AI model and generating analysis results, and means for notifying a manager of the analysis results. This makes it possible to efficiently collect complaints from employees and analyze them using the generative AI model, thereby providing quick feedback to the manager and enabling them to take concrete action to improve business operations.

[1044] A "means for collecting opinions on business improvement" is a method or device for collecting opinions on business improvement from employees.

[1045] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints when no suggestions for improving work are given from employees.

[1046] The "means for collecting complaints as a measure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to give advice on business improvement.

[1047] The "means for providing an interface for inputting complaints" refers to a method or device that provides a user interface for employees to input complaints.

[1048] The "means for storing input complaints in a database" refers to a method or device for storing complaints input by employees in a database.

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

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

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

[1052] Hardware and software used

[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 complaint input interface:

[1057] The server runs a web server using Flask and serves a web page on a touchscreen display with a text box and a submit button, allowing employees to enter their complaints using this interface.

[1058] 2. Complaint Data Collection:

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

[1060] 3. Dissatisfaction analysis:

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

[1062] 4. Reporting to the Administrator:

[1063] The server notifies the administrator of the generated analysis results by email, using the Python smtplib library to send the email.

[1064] Specific examples

[1065] For example, a factory worker types "machine maintenance is overdue" and clicks 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 complaints: Machine maintenance is delayed

[1067] Prompt generative AI model: Generate improvement suggestions for this complaint.

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

[1069] In this way, employee complaints can be efficiently collected and analyzed using generative AI models, providing quick feedback to managers and enabling them to take concrete actions to improve operations.

[1070] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1071] Step 1:

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

[1073] Step 2:

[1074] The server receives the complaint data entered when the user clicks the submit button. The input is the complaint data sent by the user, and the output is to save the 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. As input, the complaint data stored in the database is used, and as output, the retrieved complaint data is input to the generative AI model. Specifically, the server executes an SQL query to retrieve complaint data from the database.

[1077] Step 4:

[1078] The server inputs the acquired dissatisfaction data into the generative AI model to generate analysis results. The inputs are the acquired dissatisfaction data and prompt sentences, 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 dissatisfaction data as prompt sentences.

[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 to be sent to the administrator. Specifically, the server uses Python's smtplib library to send an email containing the analysis results to the administrator.

[1081] In this way, complaints from employees can be efficiently collected, analyzed using a generative AI model, and the results communicated to managers, enabling them to take concrete action to improve operations.

[1082] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1083] "Example 1"

[1084] In one embodiment of the present invention, an emotion engine is incorporated. 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 components 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 proposing business improvements.

[1085] "Example 2"

[1086] The emotion engine also has the function of tracking changes in the user's emotions. Specifically, if a user inputs their opinions or complaints multiple times, the emotion engine tracks changes in the user's emotions from those inputs. For example, the emotion engine tracks changes in emotions, such as when a user feels dissatisfied when they first input their opinion, but after several inputs, they start to feel satisfied. The results of tracking changes in emotions are used as reference information when updating proposals for business improvement.

[1087] "Example 3"

[1088] Furthermore, the emotion engine takes the user's emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement proposals based on the results of analyzing the user's emotions and tracking changes in emotions. For example, for a task that the user is dissatisfied with, the emotion engine makes proposals to resolve that dissatisfaction. Also, for a task that the user is satisfied with, the emotion engine makes proposals to maintain that satisfaction. In this way, the emotion engine makes it possible to propose business improvements that take the user's emotions into consideration.

[1089] The processing flow of each embodiment will be described below.

[1090] "Example 1"

[1091] Step 1: The user inputs their ideas for business improvement.

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

[1093] Step 3: Make business improvement proposals based on the analysis results.

[1094] "Example 2"

[1095] Step 1: The user enters their opinion or complaint multiple times.

[1096] Step 2: The emotion engine tracks the emotional changes from the user's input.

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

[1098] "Example 3"

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

[1100] Step 2: For tasks that users are dissatisfied with, make suggestions to resolve the dissatisfaction.

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

[1102] Example 1

[1103] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1104] Conventional business improvement systems lack the means to effectively collect and analyze user opinions and complaints, which means that business improvement proposals are not made appropriately. In addition, because analysis does not take into account user emotions, it is difficult to propose improvements that reflect the user's true intentions and emotions.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1106] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for sending the received opinions to a sentiment analysis engine, means for the sentiment analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for proposing business improvement based on the analysis results. This makes it possible to effectively collect user opinions and complaints and, through sentiment analysis, to propose business improvement that reflects the user's true intentions and emotions.

[1107] The "means for collecting opinions on business improvement" is a function that provides an interface for users to input improvement suggestions for business operations and send them to the system.

[1108] The "means for collecting complaints" is a function that provides an interface for users to input complaints or problems about work and send them to the system.

[1109] The "means for receiving opinions input by users" is a function by which the server receives opinions sent by users via the web interface.

[1110] The "means for transmitting received opinions to a sentiment analysis engine" is a function for transmitting user opinions received by the server to a sentiment analysis engine.

[1111] "Means for the emotion analysis engine to analyze opinions and extract emotional elements" is a function in which the emotion analysis engine analyzes user opinions and extracts emotional elements from them.

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

[1113] "Means for proposing business improvements based on analysis results" is a function that generates proposals for business improvements based on saved analysis results and notifies managers and related parties.

[1114] MODE FOR CARRYING OUT THE INVENTION

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

[1116] System configuration

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

[1118] Hardware and software used

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

[1120] System Operation

[1121] A user accesses the system's web interface using a web browser. The user enters their opinion in a text box for entering opinions or complaints about business improvement and clicks the "Submit" button. For example, the user may enter "This task takes too long."

[1122] The server receives the opinions sent by the user as an HTTP request. The server analyzes this request and extracts the text data of the opinions. The extracted text data of the opinions is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinions 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 by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The server then makes business improvement proposals based on the stored analysis results. For example, if multiple users have similar complaints, the server generates proposals for reviewing the business process and notifies the administrator.

[1124] Examples of concrete examples and prompts

[1125] For example, a user accesses a web interface and types, "This task takes too long." The server receives this comment and sends it to a sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" or "stress" and returns the results to the server. The server stores the analysis results in a database and uses them later to make suggestions for improving the task.

[1126] Example prompt sentence:

[1127] "A user has entered an idea for improving business operations. Please use the sentiment engine to analyze the sentiment of this idea and save the results."

[1128] In this way, a system is realized in which the server, terminals, and users each play their respective roles, effectively collecting and analyzing opinions for business improvement.

[1129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1130] Step 1:

[1131] User enters their opinion

[1132] A user accesses the system's web interface using a web browser. The user enters their opinion or complaint about business improvement in the text box and clicks 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 opinion

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

[1136] Step 3:

[1137] The server sends the opinion to the sentiment analysis engine

[1138] The server sends the extracted text data of opinions to a 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 text data of opinions, and the output is the data sent to the sentiment analysis engine.

[1139] Step 4:

[1140] Sentiment analysis engine analyzes opinions

[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 the opinion "This task takes too much time." The input data is the text data of the opinions, and the output is the results of the sentiment analysis.

[1142] Step 5:

[1143] The server stores the analysis results

[1144] The server receives the analysis results returned by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The input data is the sentiment analysis results, and the output is the data stored in the database.

[1145] Step 6:

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

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

[1148] (Application example 1)

[1149] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1150] Conventional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. They also lacked the functionality to analyze the emotional components of collected opinions and complaints and provide feedback to managers in real time, making it difficult to quickly improve business operations.

[1151] The identification process by the identification 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of the 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] The "means for collecting opinions on work improvement" is a function that provides an interface for workers to input improvement suggestions regarding work.

[1153] The "means of collecting complaints as a countermeasure when no suggestions for work improvement are made" is a function that provides an interface for workers to input complaints about their work.

[1154] The "means of collecting complaints as a countermeasure when there is not enough time to give advice on business improvement" is a function that provides an interface for collecting complaints from workers when there is not enough time to give advice on business improvement.

[1155] "Means for analyzing the emotional components of opinions entered using an emotion analysis engine" means software or algorithms for analyzing the emotional components of opinions or complaints entered by workers.

[1156] "Means for notifying analysis results in real time" is a function for immediately notifying factory managers of the results of analysis by the emotion analysis engine.

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

[1158] As an embodiment of the present invention, a system is provided that collects opinions on work improvement and complaints from workers in a factory, analyzes the 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] Device: A smartphone or tablet where workers can input their opinions and complaints.

[1163] Network: The Internet or local network for connecting the server and devices.

[1164] software

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

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

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

[1168] Processing flow

[1169] 1. Feedback collection: Workers use their smartphones or tablets to access a web-based interface and enter their feedback and complaints about work improvements.

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

[1171] 3. Sentiment analysis: The sentiment analysis engine on the server analyzes the emotional components 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: The analysis results, as well as the entered opinions and complaints, are stored in a database.

[1174] Specific examples

[1175] For example, if a worker inputs an opinion such as, "This machine breaks down frequently, causing work delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." The results of this analysis are immediately notified to the factory manager and saved in a database.

[1176] Prompt Sentence Examples

[1177] Analyze the opinions entered by workers using a sentiment analysis engine to extract emotional elements. For example, extract emotions such as "dissatisfaction" and "stress" from the opinion, "This machine breaks down frequently, causing work delays."

[1178] In this way, it is possible to efficiently collect opinions and complaints from workers, analyze their emotional factors, and provide feedback to managers in real time, which is expected to lead to rapid improvements in operations.

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] Users access a web-based interface using a smartphone or tablet and enter their opinions on business improvements or complaints. The entered data is saved in text format on the device.

[1182] Step 2:

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

[1184] Step 3:

[1185] The server passes the received opinions and complaints to a sentiment analysis engine, which uses the Natural Language Toolkit (NLTK) to analyze the emotional components 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 sent via email or a dedicated manager dashboard.

[1188] Step 5:

[1189] The server stores the input opinions and complaints along with the analysis results in a database, including the text of the opinion or complaint, its sentiment score, and a timestamp.

[1190] Step 6:

[1191] Based on the analysis results, factory managers can consider specific measures to improve operations. Managers can also access the database to view the history of past opinions and complaints.

[1192] In this way, it is possible to efficiently collect opinions and complaints from workers, analyze their emotional factors, and provide feedback to managers in real time, which is expected to lead to rapid improvements in operations.

[1193] Example 2

[1194] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1195] Conventional business improvement systems lack the means to effectively collect and analyze user opinions and complaints, which can lead to problems with inappropriate business improvement proposals. Furthermore, the lack of a function to track changes in user emotions makes it difficult to accurately evaluate the effectiveness of business improvement. This often results in business improvement proposals that are not in line with users' actual feelings or needs.

[1196] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1197] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, emotion analysis means for analyzing opinions and complaints entered by users, means for tracking changes in user 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 by tracking changes in user emotions, more appropriate business improvement proposals can be made.

[1198] "Means for collecting opinions on business improvement" refers to interfaces and functions for collecting improvement suggestions related to business from users.

[1199] "Means for collecting complaints" refers to an interface or function that allows users to input complaints or problems related to work and send them to the system.

[1200] "Sentiment analysis means" refers to the technology or algorithms used to analyze text data of opinions and complaints entered by users and extract emotions from it.

[1201] "Means for tracking changes in emotions" refers to technologies and algorithms for tracking changes in a user's emotions based on opinions and complaints entered by the user multiple times.

[1202] "Means for updating business improvement proposals" refers to functions and processes for appropriately updating the content of business improvement proposals based on data obtained from the sentiment analysis means and the means for tracking changes in sentiment.

[1203] The present invention is a system for effectively collecting and analyzing opinions and complaints about business improvement. A specific embodiment of this system will be 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: The device on which a user enters their opinion or complaint. This can include a computer, smartphone, or tablet.

[1208] Sentiment analysis engine: Software for analyzing user input data and extracting emotions. As a concrete example, we will use IBM Watson Natural Language Understanding.

[1209] Program processing

[1210] Complaint collection interface

[1211] 1. User enters complaint:

[1212] The user enters a complaint related to a particular job into a text box on the terminal.

[1213] For example, type "The meeting is too long."

[1214] 2. User submits complaint:

[1215] The user clicks the send button to send the complaint to the server.

[1216] 3. The server receives the complaint:

[1217] The server receives the complaints sent by the users and stores them in a database.

[1218] Tracking emotions with the emotion engine

[1219] 1. The server passes the user's input to the emotion 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 parse 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 "dissatisfied."

[1224] 3. The server tracks changes in emotions:

[1225] The server passes the user's input data over multiple times to the emotion engine and tracks changes in emotion.

[1226] Example: Tracking the change in emotion from "dissatisfied" on the first entry to "satisfied" after a few entries.

[1227] 4. The server reflects changes in emotions in business improvement proposals:

[1228] The server updates its business improvement proposals based on data on changes in emotions obtained from the emotion engine.

[1229] Example: Reflect the feedback that "the satisfaction has increased because the meeting time has been shortened" in proposals for operational improvement.

[1230] Examples and prompts

[1231] Specific examples

[1232] User input: "Meeting is too long"

[1233] Emotion engine analysis results: "Dissatisfied" after the first input, "Satisfied" after a few inputs

[1234] Suggested business improvement: "By shortening meeting times, user satisfaction will improve."

[1235] Prompt Sentence Examples

[1236] "Enter a specific work-related complaint. Example: 'Meetings are too long.'"

[1237] "Please tell us how your feelings have changed regarding recent improvements in your work. Example: 'I'm happy that meeting times have been reduced.'"

[1238] This system collects user complaints and uses an emotion engine to track changes in emotions, thereby achieving more effective business improvements.

[1239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1240] Step 1:

[1241] User enters complaint

[1242] Input: The user enters a specific job-related complaint into a text box on the terminal.

[1243] What happens: The user types "meetings are too long" into a text box on their browser.

[1244] Output: Generates text data of the input complaint.

[1245] Step 2:

[1246] User submits complaint

[1247] Input: The text data of the complaint entered by the user.

[1248] Specific action: The user clicks the "Submit" button.

[1249] Output: The text of the complaint is sent to the server.

[1250] Step 3:

[1251] The server receives the complaint

[1252] Input: The text of the complaint submitted by the user.

[1253] What happens: The server receives the HTTP POST request and stores the complaint "meeting is too long" in a database.

[1254] Output: Complaint text data stored in a database.

[1255] Step 4:

[1256] The server passes the user's input to the emotion engine.

[1257] Input: Complaint text data stored in a database.

[1258] What happens: The server sends an API request to the emotion engine to parse the text "The meeting is too long."

[1259] Output: Text data sent to the emotion engine.

[1260] Step 5:

[1261] Emotion engine analyzes emotions

[1262] Input: Text data sent to the emotion engine.

[1263] What it does: The emotion engine uses natural language processing techniques to analyze emotions from text.

[1264] Output: Sentiment analysis result of "Dissatisfied".

[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 "dissatisfaction" at the first input to "satisfaction" after several inputs.

[1269] Output: Emotion change data.

[1270] Step 7:

[1271] The server reflects changes in emotions in business improvement proposals

[1272] Input: Emotion change data.

[1273] Specific operation: The server reflects the feedback that "satisfaction has increased because the meeting time has been shortened" in its business improvement proposals.

[1274] Output: Updated business improvement proposals.

[1275] (Application example 2)

[1276] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1277] Conventional business improvement systems have difficulty effectively collecting workers' opinions and complaints and tracking changes in their emotions. Furthermore, they are unable to generate specific improvement proposals based on the collected opinions and complaints, limiting the effectiveness of business improvement. In particular, they are unable to properly reflect the feedback of workers working in factories, resulting in delays in improving the work environment and machine operation.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1279] In this invention, the server includes: means for receiving opinions or complaints about machine operation or the work environment input from each worker working in the factory; means for recognizing a score indicating the worker's emotion from the input opinions or complaints using an emotion engine; prompts for generating improvement proposals for the machine or the work environment based on the input opinions or complaints and the scores of each worker; and means for generating improvement proposals for the machine or the work environment using a generative AI. The server further includes means for tracking changes in the scores of each worker, and the improvement proposal generating means generates the improvement proposals for the machine or the work environment based on the input opinions or complaints and the changes in the scores of each worker using the generative AI. This effectively collects worker feedback and tracks changes in emotion, thereby generating specific improvement proposals and enabling rapid and effective business improvement. The server may further include means that is installed on a machine in a factory and provides an interface for the worker to input the opinion or complaint.

[1280] "Means for collecting opinions on business improvement" refers to methods or devices for collecting suggestions and opinions on improving the efficiency and quality of business from workers and related parties.

[1281] A "means for collecting complaints" is a method or device for collecting complaints and problems that workers and other related parties have about their work or work environment.

[1282] "Means for providing an interface that allows workers to input their complaints and opinions about machine operation and the work environment" refers to a method or device that provides a user interface that allows workers to input their own opinions and complaints and send them to the 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] The "means for generating improvement proposals for machine operation and working environment based on changes in emotions" refers to a method or device for generating specific improvement proposals 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 a worker, and an emotion engine. The server includes means for collecting opinions on business improvement, means for collecting complaints, means for providing an interface through which workers can input their complaints and opinions about machine operation and the work environment, the emotion engine, and means for generating improvement proposals for machine operation and the work environment based on changes in emotion.

[1286] Hardware and software 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 calculation

[1290] The server receives the feedback text entered by the worker through the terminal. The feedback text is subjected to sentiment analysis using TextBlob to calculate a positive or negative score. This sentiment score is saved in JSON format along with the feedback data. The server tracks the changes in the sentiment of a specific worker and generates specific improvement suggestions based on this.

[1291] Specific examples

[1292] For example, if a worker enters feedback such as "The robot's movements are slow," TextBlob analyzes this text and calculates a negative emotion score. Next, if the worker enters feedback such as "The robot's movements have improved recently," a positive emotion score is calculated. The server tracks these emotion scores and generates suggestions for improving the robot's movements and working environment based on the changes in emotion.

[1293] Prompt Sentence Examples

[1294] User ID: user123

[1295] Feedback: The robot is slow.

[1296] Sentiment score: -0.5

[1297] User ID: user123

[1298] Feedback: Recently, the robot's behavior has improved.

[1299] Sentiment score: 0.8

[1300] In this way, the feedback system effectively collects workers' opinions and complaints, tracks changes in their emotions, and generates specific improvement suggestions, enabling rapid and effective improvements to operations.

[1301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1302] Step 1:

[1303] A user who is a worker working in a factory uses a terminal to input feedback expressing opinions or dissatisfaction with the operation of a machine or the working environment.

[1304] Input: User enters feedback in the text box and clicks the submit button.

[1305] Output: The feedback text is sent to the server.

[1306] Specific action: The user types "The robot is slow" and clicks the submit button. It may also be installed on machines in a factory to provide an interface for workers to input their opinions or complaints.

[1307] Step 2:

[1308] The server receives the feedback text.

[1309] Input: The feedback text submitted by the user.

[1310] Output: The feedback text is saved on the server.

[1311] Specific behavior: The server receives the feedback text "The robot is slow" and stores it in the database.

[1312] Step 3:

[1313] The server passes the feedback text to the emotion engine for analysis.

[1314] Input: The saved feedback text.

[1315] Output: Sentiment score (positive or negative score).

[1316] What happens: The server uses TextBlob to parse the text "The robot is slow" and calculates a negative sentiment score (e.g., -0.5).

[1317] Step 4:

[1318] The server stores the emotion scores along with the feedback data in JSON format.

[1319] Input: Sentiment score and feedback text.

[1320] Output: Feedback data in JSON format.

[1321] Specific operation: The server saves the data "User ID: user123, Feedback: Robot is slow, Emotion score: -0.5" in JSON format.

[1322] Step 5:

[1323] The server tracks the emotional changes of a particular user.

[1324] Input: Stored feedback data.

[1325] Output: Data showing changes in user emotions.

[1326] What happens: The server analyzes the past feedback data of "user123" and tracks changes in the emotion score.

[1327] Step 6:

[1328] The server generates suggestions for improving the machine's operation and working environment based on changes in emotions.

[1329] Input: User emotion change data.

[1330] Output: Improvement suggestions.

[1331] Specific operation: The server generates improvement proposals for the machine or the work environment based on the feedback data representing the input opinions or dissatisfaction and the changes in each worker's score, using a prompt sentence instructing the server to generate improvement proposals for the machine or the work environment and a generative AI. For example, the server generates a suggestion to improve the robot's movement speed based on the change in the emotion score of "user123" from negative to positive. Example prompt sentence: "User ID: user123, Feedback: The robot is slow, Sentiment score: -0.5, User ID: user123, Feedback: The robot's behavior has been improved recently., Emotion score: 0.8 Based on this feedback data and changes in sentiment scores, generate improvement recommendations for machinery or the working environment within the factory.

[1332] Example 3

[1333] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1334] Conventional business improvement systems do not provide suggestions that fully consider users' dissatisfaction and feelings, limiting the effectiveness of business improvement. Furthermore, there are insufficient measures in place for cases where no suggestions for business improvement are made or there is no time to provide advice. This leaves users dissatisfied and prevents business efficiency from improving.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1336] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are received, means for collecting complaints as a countermeasure when there is no time to give advice on business improvement, emotion analysis means for analyzing the complaints entered by the user, generative model means for generating business improvement proposals based on the emotion analysis results, and means for returning the generated proposals to the user. This makes it possible to propose business improvements that take the user's emotions into consideration, effectively resolve the user's complaints, and improve business efficiency.

[1337] The "means for collecting opinions on business improvement" is a means for providing an interface that enables users to input improvement suggestions related to business operations and send them to the system.

[1338] The "means for collecting complaints as a measure when no suggestions for business improvement are given" is a means for providing an interface that allows users to input complaints about business and send them to the system.

[1339] The "means for collecting complaints as a measure when there is no time to give advice on business improvement" is a means for providing an interface that allows users to input complaints about business and send them to the system.

[1340] The "emotion analysis means for analyzing dissatisfaction input by the user" is a means for analyzing text data input by the user and determining the emotion.

[1341] The "generative model means for generating proposals for business improvement based on the results of emotion analysis" is a means for generating specific proposals for business improvement based on the results of emotion analysis.

[1342] The "means for returning the generated proposal to the user" is a means for notifying the user of the business improvement proposal generated by the generative model means.

[1343] The present invention is a system for collecting users' work-related complaints, performing emotion analysis, and generating business improvement proposals based on the results. Specific embodiments of this system are described below.

[1344] The server provides an interface for users to input their opinions and complaints about work. This interface is built using HTML and JavaScript, and users access it through a web browser. Users enter their complaints about work in a text box and click the send button to submit their complaints.

[1345] The device sends the user's complaints to the server using an AJAX request, and the server stores the complaints in a MySQL database using the Python Flask framework to connect to the database and execute an INSERT query.

[1346] The server passes the saved dissatisfaction data to the sentiment analysis means. The sentiment analysis means is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The sentiment analysis means analyzes the text data entered by the user and determines their sentiment.

[1347] The sentiment analysis means passes data to the generative model means, which generates business improvement proposals based on the analysis results. The generative model means generates proposals using a generative AI model. Specifically, a prompt sentence is input into a generative AI model such as GPT-3, and a proposal is generated. For example, if a user inputs "writing daily reports is a hassle," the following prompt sentence is input into the generative AI model:

[1348] A user has entered "It's a hassle to create reports every day." Please provide suggestions for business improvements to resolve this issue.

[1349] The generative AI model suggests "introducing an automated report creation tool."

[1350] The server returns the generated suggestions to the user. Specifically, the suggestions are returned as a response to the AJAX request, and the terminal displays them to the user. The user can then check the suggestions on their browser and take specific actions to improve their work.

[1351] In this way, the system can propose improvements to the business taking into account the user's feelings, effectively alleviating the user's dissatisfaction and improving business efficiency. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1352] Step 1:

[1353] The user enters a complaint.

[1354] A user accesses the system's interface using a web browser. The interface displays a text box and a submit button. The user enters a complaint about work in the text box and clicks the submit button. The input data is the complaint text entered by the user. The output is the event of clicking the submit button.

[1355] Step 2:

[1356] The device sends the complaint to the server.

[1357] The terminal sends the complaints entered by the user to the server using an AJAX request. Specifically, JavaScript is used to asynchronously send the entered text data to the server. The input data is the complaint text entered by the user. The output is the complaint data sent to the server.

[1358] Step 3:

[1359] The server stores the complaint in a database.

[1360] The server stores the received complaint data in a MySQL database. Specifically, it uses the Python Flask framework to connect to the database and execute INSERT queries. 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 means.

[1363] The server passes the stored dissatisfaction data to the sentiment analysis means. The sentiment analysis means is implemented in Python and uses the natural language processing library NLTK and the sentiment analysis library TextBlob. The input data is the dissatisfaction data stored in the database. The output is the dissatisfaction data passed to the sentiment analysis means.

[1364] Step 5:

[1365] A sentiment analysis tool analyzes dissatisfaction.

[1366] The sentiment analysis means 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 it as "dissatisfied." The input data is the dissatisfaction data passed to the sentiment analysis means. The output is the analyzed sentiment data.

[1367] Step 6:

[1368] The sentiment analysis means passes the data to a generative model means that generates business improvement suggestions.

[1369] The sentiment analysis means passes data to a generative model means, which generates business improvement proposals based on the analysis results. The generative model means generates proposals using a generative AI model. Specifically, a prompt sentence is input into a generative AI model such as GPT-3 to generate proposals. The input data is the analyzed sentiment data. The output is the sentiment data passed to the generative model means.

[1370] Step 7:

[1371] A generative model means generates business improvement proposals.

[1372] The generative model generates specific suggestions for business improvement based on the results of sentiment analysis. For example, if a user inputs "It's a hassle to write reports every day," the following prompt sentence is input to the generative AI model:

[1373] A user has entered "It's a hassle to create reports every day." Please provide suggestions for business improvements to resolve this issue.

[1374] The generative AI model proposes "introducing an automated tool for report creation." The input data is emotion data passed to the generative model. The output is a generated proposal for business improvement.

[1375] Step 8:

[1376] The server returns the suggestions to the user.

[1377] The server returns the generated proposal to the user. Specifically, the proposal is returned as a response to the AJAX request, and the terminal displays it to the user. The input data is the generated proposal for business improvement. The output is the proposal returned to the user.

[1378] In this way, the system can propose work improvement measures that take the user's feelings into consideration, effectively alleviating the user's dissatisfaction and improving work efficiency.

[1379] (Application example 3)

[1380] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1381] With conventional business improvement systems, it was difficult to efficiently collect complaints and opinions from workers, analyze their emotions, and propose appropriate business improvements. Furthermore, when a worker was feeling dissatisfied, there was a lack of a way to accurately grasp their emotions and quickly respond. This led to problems such as a decline in work efficiency and a drop in worker motivation.

[1382] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1383] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for collecting complaints by voice input or text input, means for analyzing the emotions of the collected complaints, and means for making suggestions for business improvement based on the results of the emotion analysis. This makes it possible to efficiently collect complaints and opinions from workers, analyze their emotions, and make appropriate suggestions for business improvement.

[1384] "Means for collecting opinions on business improvement" refers to an interface or method for collecting improvement suggestions for business operations from workers and users.

[1385] "Means for collecting complaints as a countermeasure when no suggestions for business improvement are received" refers to an interface or method for collecting complaints from workers or users when no suggestions for business improvement are received.

[1386] "Means for collecting complaints as a countermeasure when there is no time to give advice on business improvement" refers to an interface or method for collecting complaints from workers or users when there is insufficient time to give advice on business improvement.

[1387] A "voice or text complaint collection means" is an interface or method that allows a worker or user to input a complaint by voice or text and transmit it to the system.

[1388] The "means for analyzing emotions from collected complaints" refers to an algorithm or method for analyzing the content of collected complaints and evaluating the emotions of workers or users.

[1389] The "means for proposing business improvement based on the results of sentiment analysis" refers to an algorithm or method for generating appropriate business improvement proposals based on the results of sentiment analysis and providing them to workers or users.

[1390] To implement this invention, the following system configuration is required: The system includes means for collecting opinions on business improvement, means for collecting complaints, means for collecting complaints by voice input or text input, means for emotionally analyzing the collected complaints, and means for proposing business improvement based on the results of the emotional analysis.

[1391] Hardware and software used

[1392] Hardware

[1393] Microphone: Used to collect voice input from the worker or user.

[1394] Factory robot: Acts as an interface to collect complaints and opinions from workers and make suggestions for improving operations.

[1395] software

[1396] Python: A programming language used to implement the program.

[1397] speech_recognition library: Used to convert voice input to text.

[1398] TextBlob library: Used to perform sentiment analysis of text.

[1399] The transformers library provides generative AI models for advanced sentiment analysis.

[1400] Data processing and calculation

[1401] Voice Recognition

[1402] The server collects the voice of the worker or user through a microphone and converts the voice into text using the speech_recognition library, which allows the voice input to be captured in 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 to analyze the sentiment, and outputs the analysis results as a sentiment label: positive, negative, or neutral.

[1405] Business improvement proposals

[1406] The server generates appropriate business improvement proposals based on the results of emotion analysis. For example, if negative emotions are detected, the server makes specific proposals to alleviate the dissatisfaction. If positive emotions are detected, the server makes proposals to maintain the satisfaction.

[1407] Specific examples

[1408] When a worker says to the robot, "This machine is difficult to operate," the robot collects the voice and sends it to a server. The server converts the voice into text and performs emotional analysis. If the analysis result is "negative," the robot will suggest, "We suggest revising the work procedures to alleviate your dissatisfaction with this task."

[1409] Prompt Sentence Examples

[1410] Worker: This machine is difficult to operate.

[1411] Robot: Recognized text: This machine is difficult to operate

[1412] Robot: Suggestion: To resolve the dissatisfaction with this job, I suggest reviewing the work procedures.

[1413] In this way, the system can efficiently collect complaints and opinions from workers, analyze their emotions, and make appropriate suggestions for improving operations.

[1414] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1415] Step 1:

[1416] The user vocalizes their complaint.

[1417] The user inputs their complaints by voice through a microphone, and the input voice data is sent from the microphone to the server.

[1418] Step 2:

[1419] The server converts the voice data into text data.

[1420] The server uses the speech_recognition library to convert the received voice data into text data. The input is voice data and the output is text data.

[1421] Step 3:

[1422] The server performs sentiment analysis on the text data.

[1423] The server uses the sentiment analysis model from the transformers library to analyze the sentiment of text data. The input is the text data, and the output is a sentiment label (positive, negative, neutral).

[1424] Step 4:

[1425] The server generates business improvement proposals based on the results of the sentiment analysis.

[1426] The server generates appropriate business improvement proposals based on the results of emotion analysis. For example, if negative emotions are detected, it makes specific proposals to alleviate the dissatisfaction. The input is emotion labels, and the output is business improvement proposals.

[1427] Step 5:

[1428] The server provides the user with suggestions for improving business operations.

[1429] The server provides the generated business improvement proposals to the user. Specifically, the proposals are displayed or read aloud through the robot's display or voice output. The input is the business improvement proposal, 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1431] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1432] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1433] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1434] [Third embodiment]

[1435] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1436] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1437] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1438] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1439] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1440] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1441] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1442] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1443] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1444] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1445] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1446] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1447] "Example 1"

[1448] One embodiment of the present invention is a system that provides a web-based interface for soliciting opinions related to a specific business as a means for soliciting opinions for business improvement. This interface allows users to input their own opinions and send them to the system.

[1449] "Example 2"

[1450] Furthermore, as a countermeasure when no suggestions for business improvement are received, an interface for collecting complaints related to specific business operations is also provided. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[1451] "Example 3"

[1452] Furthermore, as a countermeasure for cases where there is no time to provide advice on business improvement, an interface is also provided for collecting complaints related to specific business operations. This interface allows users to input their own complaints and send them to the system. Specifically, a complaint related to a business operation can be sent by entering the complaint in the text box and clicking the send button.

[1453] The processing flow of each embodiment will be described below.

[1454] "Example 1"

[1455] Step 1: The user accesses the web-based interface.

[1456] Step 2: The user enters their opinion related to a specific task.

[1457] Step 3: The user submits their input to the system.

[1458] "Example 2"

[1459] Step 1: The user accesses the web-based interface.

[1460] Step 2: The user enters a specific job-related complaint.

[1461] Step 3: The user submits their complaint to the system.

[1462] "Example 3"

[1463] Step 1: The user accesses the web-based interface.

[1464] Step 2: The user enters a specific job-related complaint.

[1465] Step 3: The user submits their complaint to the system.

[1466] Example 1

[1467] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1468] Conventional business improvement systems have difficulty efficiently collecting user opinions and complaints and proposing appropriate improvement measures. Furthermore, if managers do not have time to provide advice on business improvement, users' dissatisfaction accumulates, resulting in a decline in business efficiency. Furthermore, there is a lack of means to effectively manage the collected opinions and complaints and derive appropriate improvement measures.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1470] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting 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 send opinions, means for saving the input opinions in a database, means for displaying the saved opinions on an administration screen, and means for an administrator to input prompt sentences to the generative AI model and obtain suggestions. This makes it possible to efficiently collect opinions and complaints from users and quickly propose appropriate improvement measures.

[1471] The "means for collecting opinions on business improvement" is a function for collecting suggestions and opinions from users regarding the efficiency and improvement of business operations.

[1472] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function for collecting complaints and problems instead of users when they do not submit suggestions for business improvement.

[1473] The "means of collecting complaints as a measure when there is no time to give advice on business improvement" is a function for collecting complaints and problems from users when the administrator does not have time to give advice on business improvement.

[1474] A "means for providing a web-based interface" is a function for providing web pages and applications that can be accessed by users via the Internet.

[1475] "Means for users to input and send opinions" is a function that allows users to input their own opinions and suggestions and send them to the system.

[1476] The "means for saving input opinions in a database" is a function for recording and saving opinions and suggestions sent by users in a database.

[1477] The "means for displaying saved opinions on the management screen" is a function for displaying the opinions and suggestions saved in the database on the management screen so that the administrator can check them.

[1478] "A means for an administrator to input prompt statements into a generative AI model and obtain suggestions" is a function that allows an administrator to input specific questions or instructions into a generative AI model and obtain suggestions or answers from the AI ​​based on those questions or instructions.

[1479] MODE FOR CARRYING OUT THE INVENTION

[1480] The present invention is a system for efficiently collecting opinions on business improvement and proposing appropriate improvement measures. A specific embodiment of this system will be described below.

[1481] System configuration

[1482] server

[1483] The server uses web server software such as Apache or Nginx to provide a web-based interface, and has the functionality to receive opinions submitted by users and store them in a database, using a database management system such as MySQL or PostgreSQL.

[1484] Terminal

[1485] The terminal is a device that allows a user to access the web interface and input their opinions. The terminal connects to the server using a browser, enters their opinions in the opinion input form, and sends the data to the server by pressing the send button.

[1486] User

[1487] Users use the system to provide suggestions for business improvement. Users access the web interface, enter their suggestions in the suggestion input form, and submit their suggestions by pressing the send button.

[1488] Program processing

[1489] The server provides a web page for users to access. This web page is created using HTML, CSS, and JavaScript. When a 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 that can be accessed by administrators. This administration screen is built using server-side scripts such as PHP and Python.

[1491] The administrator checks user feedback on the management screen and, if necessary, inputs prompts into the generative AI model, which then suggests appropriate improvements and next actions based on the input prompts.

[1492] Specific examples

[1493] For example, consider a user who wants to input an opinion such as "Please consider introducing a new project management tool." The user accesses a web interface, enters this information into the opinion input form, and presses the submit button. The server receives this data and stores it in a database.

[1494] The administrator confirms this opinion in the management screen and enters the following prompt for the generative AI model:

[1495] A user has submitted a request to "consider introducing a new project management tool." How should we handle this request and what improvements should we suggest?

[1496] The generative AI model then suggests appropriate improvements based on the prompt, such as, "Task management efficiency could be improved by introducing a new project management tool. Specifically, consider using a task management tool."

[1497] In this way, it becomes possible to efficiently collect user opinions and quickly propose appropriate improvement measures.

[1498] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1499] Step 1:

[1500] The user accesses the web interface.

[1501] What happens: The user enters a URL into the browser's address bar and presses Enter.

[1502] Input: Access request from the user's browser.

[1503] Output: A web page is displayed.

[1504] Data processing / calculation: The server uses web server software such as Apache or Nginx to send the HTML, CSS, and JavaScript files corresponding to the specified URL to the user's browser.

[1505] Step 2:

[1506] The user enters their opinion in the opinion input form and presses the send button.

[1507] Specific behavior: The user uses the keyboard to type in their opinion and then uses the mouse to click the submit button.

[1508] Input: User-entered opinion.

[1509] Output: Submit button click event.

[1510] Data processing / calculation: The browser uses JavaScript to retrieve the user's input data and send it to the server as an HTTP POST request.

[1511] Step 3:

[1512] The terminal sends the input data to the server.

[1513] Specific behavior: The device sends data using the JavaScript fetch API.

[1514] Input: Opinion data entered by the user.

[1515] Output: HTTP POST request to the server.

[1516] Data processing / calculation: The terminal converts the opinion data entered by the user into JSON format, includes it in the body of an HTTP POST request, and sends it to the server.

[1517] Step 4:

[1518] The server stores the received data in a database.

[1519] How it works: The server receives data using Python's Flask framework and stores it in a database using SQLAlchemy.

[1520] Input: Opinion data included in the body of the 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 saved opinions on the administration screen.

[1525] Specific operation: The server generates an administration screen using an HTML template engine and displays the saved opinions.

[1526] Input: Opinion data stored in a database.

[1527] Output: HTML page of the admin panel.

[1528] Data processing / calculation: The server retrieves opinion data from the database, embeds it in an HTML template, and generates an administration screen.

[1529] Step 6:

[1530] The administrator checks the opinions on the management screen and, if necessary, inputs prompt text into the generative AI model.

[1531] Specific operation: The administrator enters a prompt text in the text box on the administration screen and clicks the send button.

[1532] Input: The prompt text entered by the administrator.

[1533] Output: The request to the generative AI model.

[1534] Data processing / calculation: Converting the prompt text entered by the administrator into an appropriate format for sending to the generative AI model.

[1535] Step 7:

[1536] A generative AI model returns suggestions.

[1537] Specific operation: The generative 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 / computation: The generative AI model analyzes the prompt and generates text that suggests appropriate improvements or next actions.

[1541] (Application example 1)

[1542] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1543] The previous system for collecting opinions for business improvement limited the means by which employees could submit their opinions, making it difficult to do so, especially within the factory. Furthermore, there was a lack of a mechanism for efficiently sending opinions to a cloud-based database, making it difficult for managers to quickly review the opinions.

[1544] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1545] In this invention, the server includes means for collecting suggestions for business improvement, means for collecting complaints as a countermeasure when no suggestions for business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice for business improvement, means that is installed on machines in the factory and provides an interface for employees to input suggestions for business improvement, and means for sending the suggestions input by employees to a cloud-based database. This enables employees to easily submit suggestions for business improvement in the factory, and the suggestions are quickly sent to the cloud-based database where managers can efficiently check them.

[1546] The "means for soliciting opinions on business improvement" is a system that provides an interface for employees to submit suggestions for improving or streamlining business operations.

[1547] A "grievance channel" is a system that provides an interface for employees to submit complaints or issues about their work.

[1548] "A means installed on machines in a factory that provides an interface for employees to input their opinions on business improvements" refers to a system that is installed on machines in a factory and includes input devices such as touch screens and keyboards that allow employees to directly input their opinions.

[1549] The "means of transmitting opinions entered by employees to a cloud-based database" refers to a system that transmits opinions entered by employees to a cloud-based database via the Internet, allowing managers to check them later.

[1550] A "cloud-based database" is a database stored on a remote server accessible via the internet, allowing multiple users to simultaneously access, manage, and share data.

[1551] A system for implementing the present invention includes a means for providing an interface installed on a machine in a factory and a means for transmitting opinions entered by employees to a cloud-based database. A specific embodiment of this system will be described below.

[1552] System configuration

[1553] The system consists of the following main components:

[1554] 1. Interfaces installed on machines in the factory:

[1555] Hardware: Touchscreen displays, keyboards, factory robots

[1556] Software: Python, Tkinter library, requests library

[1557] 2. Cloud-based database:

[1558] Hardware: Remote Server

[1559] Software: Database management system (e.g., MySQL, PostgreSQL)

[1560] Program processing

[1561] The server provides an interface for employees to input their suggestions for business improvement. Employees enter their suggestions using a touchscreen display or keyboard, and the suggestions are retrieved by a Python program. The retrieved suggestions are then sent to a cloud-based database as HTTP POST requests using the requests library.

[1562] Data processing and calculation

[1563] Data processing: The opinions entered by employees are obtained in text format. The entered text is converted into an appropriate format and prepared as the payload for the HTTP request.

[1564] Data processing: Opinions submitted to a cloud-based database are stored by a database management system and can be accessed and reviewed by administrators at a later date.

[1565] Specific examples

[1566] For example, consider a scenario in which a factory worker types in their request, "I'd like a new sensor installed to adjust the speed of the production line," and clicks the submit button. In this case, the worker's request is passed to a Python program via a touchscreen display and then sent to a cloud-based database using the requests library.

[1567] Prompt Sentence Examples

[1568] Please enter your opinion such as "We would like a new sensor 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 the suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[1570] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1571] Step 1:

[1572] A user inputs their thoughts on business improvement using a touchscreen display or keyboard installed on a machine in a factory. The inputted thoughts are sent to a terminal in text format. The input data is the text of the thoughts input by the user, and the output data is the text data stored in the terminal.

[1573] Step 2:

[1574] The device receives the input opinion and converts it into an appropriate format. Specifically, it prepares the text data as the payload of an HTTP POST request. The input data is the text data stored on the device, and the output data is the data prepared as the payload of the HTTP request.

[1575] Step 3:

[1576] The terminal uses the requests library to send an HTTP POST request to the 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 the opinion data sent to the cloud-based database and stores it in the database management system, where 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 the data through a database management system so that the administrator can later review the opinion data. The input data is the opinion data stored in the database, and the output data is the opinion data provided in a format accessible to the administrator.

[1581] In this way, users can easily submit suggestions for improving operations within the factory, and those suggestions are quickly sent to a cloud-based database where managers can efficiently review them.

[1582] Example 2

[1583] Next, a description will be given of 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."

[1584] Conventional business improvement systems were inadequate when users did not provide feedback or when there was no time to provide advice on business improvement. Furthermore, there was a lack of a means to efficiently collect and analyze user complaints, making it difficult to implement specific measures for business improvement. This resulted in problems such as delays in business efficiency and improvements.

[1585] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1586] In this invention, the server includes a means for users to input complaints, a means for users to transmit complaints, a means for the server to receive the complaints, a means for the server to store the complaints in a database, and a means for the server to analyze the complaint data, thereby enabling efficient collection and analysis of complaints from users.

[1587] "Means for collecting opinions on business improvement" refers to interfaces and functions for collecting opinions on business improvement from users.

[1588] "Means for collecting complaints as a countermeasure when no suggestions for business improvement are made" refers to interfaces and functions for collecting business-related complaints from users.

[1589] "Means for users to input complaints" refers to a text box or input field where users can enter their complaints in text form.

[1590] "Means for users to send complaints" refers to a send button or send function that allows users to send their inputted complaints to the system.

[1591] "Means by which the server receives complaints" refers to the functions and processes by which the server receives complaints sent by users.

[1592] "Means by which the server stores complaints in a database" refers to the functionality or process by which received complaints are stored in a database.

[1593] "Means by which the server analyzes the complaint data" refers to the functions and processes for analyzing the complaint data stored in the database and extracting trends and frequently occurring keywords.

[1594] This invention is a system for collecting and analyzing complaints related to a specific task when no suggestions for improving the task are made. A specific embodiment of this system will be described below.

[1595] Hardware and software used

[1596] Hardware: Servers, user devices (PCs, smartphones, etc.)

[1597] Software: Web browser, Web server (Apache, Nginx, etc.), Backend framework (Django, Flask, etc.), Database (MySQL, PostgreSQL, etc.)

[1598] System Overview

[1599] The system provides an interface for users to input work-related complaints and send them to a server, which stores the complaints in a database and periodically analyzes them.

[1600] User operations

[1601] Users access the system interface using a web browser, which displays a text box where users can enter their work-related complaints. Once they have completed the input, they click a submit button to send the complaint to the server.

[1602] Specific examples

[1603] The user types "The meeting is too long" into the text box and clicks the send button.

[1604] Server Processing

[1605] The server receives the HTTP POST request and extracts the complaint text from the request body. This process is handled by a backend framework (e.g., Django or Flask). The extracted complaint text is stored in a database. Along with the complaint text, metadata such as the submission date and user ID are also stored in the database.

[1606] Analyzing the data

[1607] The server periodically analyzes the complaint data stored in the database, uses text mining technology to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[1608] Specific examples

[1609] The server outputs the analysis result that there are many complaints that "meetings are too long."

[1610] Example prompts for generative AI models

[1611] "Analyze work-related complaints entered by users and identify the most common types of complaints."

[1612] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[1613] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1614] Step 1:

[1615] User enters complaint

[1616] Specific behavior: A user opens a web browser and accesses the system interface, which displays a text box where the user can enter their 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] What happens: The user enters their complaint into the text box and then clicks the submit button, which sends the complaint 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] Server receives complaint

[1626] What happens: The server receives the HTTP POST request and extracts the complaint text from the request body. This is handled by the backend framework (e.g., Django, Flask).

[1627] Input: Complaint text sent as an HTTP POST request

[1628] Output: Extracted complaint text

[1629] Step 4:

[1630] The server stores the complaint in a database

[1631] Specific operation: The server stores the extracted complaint text in a database, along with metadata such as the submission date and time and user ID.

[1632] Input: Extracted complaint text, submission date and time, user ID, and other metadata

[1633] Output: Complaint text and metadata stored in a database

[1634] Step 5:

[1635] The server analyzes the complaint data

[1636] How it works: The server periodically analyzes the complaint data stored in the database, uses text mining techniques to extract complaint trends and frequently occurring keywords, and uses a generative AI model to analyze how frequently specific complaints occur.

[1637] Input: Complaint data stored in the database

[1638] Output: Dissatisfaction trends, frequently occurring keywords, and analysis results

[1639] In this way, users can easily send their complaints about work to the system, which then receives and analyzes them, enabling specific measures to be taken to improve work.

[1640] (Application example 2)

[1641] Next, a description will be given of 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] With conventional business improvement systems, it was difficult to efficiently collect opinions and complaints from employees, and there was a lack of means to properly collect complaints, especially when no suggestions for business improvement were made or when there was no time to give advice on business improvement. Also, when collecting complaints within the factory, there was no interface that allowed employees to easily report their complaints, which meant that it was difficult to obtain sufficient feedback for business improvement.

[1643] The specific processing 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to give advice on business improvement, means for providing an interface for inputting and sending complaints related to specific business operations, and means for sending complaints to the server. This provides an environment in which employees can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[1644] "Means 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] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints about work when no suggestions for improving work are given from employees.

[1646] The "means for collecting complaints as a countermeasure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints about business operations from employees when there is insufficient time to give advice on business improvement.

[1647] "Means for providing an interface for inputting and transmitting complaints related to specific work" means a method or apparatus that provides a user interface for employees to input complaints related to specific work and transmit them to the system.

[1648] The "means for transmitting complaints to a server" refers to a method or device for transmitting complaints entered by employees to a server.

[1649] The system for implementing this invention provides an interface for efficiently collecting opinions and complaints about work improvements. Specifically, we will build a system that allows factory workers to easily report their work-related complaints through a robot.

[1650] System Program

[1651] The system uses Python's Tkinter library to create a GUI, which is installed on factory robots equipped with touchscreens, providing an interface for employees to input and submit complaints.

[1652] Program processing explanation

[1653] The server processes data using the following hardware and software:

[1654] Hardware: Factory robots with touchscreens

[1655] Software: Python, Tkinter library

[1656] The server retrieves the complaints entered by the employee from the text box and sends them to the server. The specific process is as follows:

[1657] 1. The employee uses the touchscreen to enter their complaint.

[1658] 2. The complaint you entered will appear in the text box.

[1659] 3. Click the submit button and your complaint will be sent to the server.

[1660] 4. The server stores the received complaint in a database and notifies the administrator.

[1661] Specific examples

[1662] If a factory worker finds a new machine too complicated to operate and is inefficient, he or she can use the robot's touchscreen to register their complaints, like this:

[1663] "The new machine is complicated to operate and work efficiency is poor."

[1664] The employee clicks the submit button and sends the complaint to the system, which then passes it on to management to consider remedial measures.

[1665] Prompt Sentence Examples

[1666] "Create an application that provides an interface to collect complaints about work efficiency in a factory. Users can enter their complaints and send them to the system by clicking a submit button."

[1667] In this way, it is possible to provide an environment where employees can easily report their dissatisfaction and efficiently collect feedback for business improvement.

[1668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1669] Step 1:

[1670] The user uses the touch screen to input their complaint.

[1671] Input: Text data (complaints) entered by the user on the touchscreen.

[1672] Output: The complaint displayed in the text box.

[1673] What it does: The user uses the touchscreen keyboard to type their complaint, which is then displayed in a text box in real time.

[1674] Step 2:

[1675] The user clicks the submit button.

[1676] Input: The user clicks the submit button.

[1677] Output: The event that the submit button was clicked.

[1678] Specific behavior: A click event occurs when the user taps the submit button on a touchscreen.

[1679] Step 3:

[1680] The device retrieves the complaint from the text box.

[1681] Input: The complaint entered in the text box.

[1682] Output: Text data of the complaints obtained.

[1683] What happens: The terminal reads the contents of the text box and stores the text data in a variable.

[1684] Step 4:

[1685] The device sends the complaint to the server.

[1686] Input: Text data of the complaints obtained.

[1687] Output: Text data of the complaint sent to the server.

[1688] Specific operation: The device generates an HTTP request and sends text data of the complaint to the server.

[1689] Step 5:

[1690] The server stores the received complaints in a database.

[1691] Input: The text of the complaint sent to the server.

[1692] Output: A record of the complaint stored in a database.

[1693] Specific behavior: The server analyzes the complaint received and stores it as a new record in the database.

[1694] Step 6:

[1695] The server notifies the administrator of the complaint.

[1696] Input: Complaint records stored in the database.

[1697] Output: The notification message sent to the administrator.

[1698] Specific operation: Based on the complaints stored in the database, the server notifies the administrator of the complaints via email or notification system.

[1699] The above steps provide an environment where users can easily report their complaints, making it possible to efficiently collect feedback for business improvement.

[1700] Example 3

[1701] Next, a third embodiment of the third embodiment will be described. 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."

[1702] Conventional business improvement systems lack the means to respond when no suggestions for business improvement are made or when there is no time to provide advice for business improvement. Another issue is that it is difficult to efficiently collect and analyze user complaints and provide specific advice for business improvement. This limits the effectiveness of business improvement and makes it difficult to increase user satisfaction.

[1703] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1704] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are submitted, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for a user to input complaints, means for a terminal to send the input 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 sentences to a generative AI model and generate advice on business improvement. This makes it possible to efficiently collect and analyze user complaints and provide specific advice on business improvement.

[1705] The "means for collecting opinions on business improvement" is a function that provides an interface for collecting improvement proposals related to business from users.

[1706] The "means of collecting complaints as a countermeasure when no suggestions for business improvement are made" is a function that provides an interface for collecting complaints about business operations instead of users submitting suggestions for business improvement.

[1707] The "means of collecting complaints as a measure when there is no time to provide advice on business improvement" is a function that provides an interface for collecting complaints about business operations from users when there is insufficient time to provide advice on business improvement.

[1708] The "means for users to input complaints" is a function that provides an interface for users to input complaints about work in text format.

[1709] The "means for transmitting the complaints entered by the terminal to the server" is a communication function for transmitting the complaints entered by the user to the server via the network.

[1710] The "means for the server to receive complaints and store them in a database" is a function for the server to receive complaints sent by users and record them in a database.

[1711] The "means for the server to analyze the complaints stored" is a function for analyzing the complaint data stored in the database and extracting specific patterns or trends.

[1712] "Means for the server to send prompt sentences to the generative AI model and generate advice for business improvement" refers to a function that enables the server to send prompt sentences to the generative AI model based on the analysis results, and the generative AI model to generate advice for business improvement.

[1713] MODE FOR CARRYING OUT THE INVENTION

[1714] The present invention is a system for efficiently collecting and analyzing opinions and complaints about business improvement and providing specific advice on business improvement. A specific embodiment of this system will be described below.

[1715] System configuration

[1716] The system consists of the following main components:

[1717] 1. User Interface: An interface for users to input their business-related opinions and complaints. It runs on a web browser and is built using HTML, CSS, and JavaScript.

[1718] 2. Terminal: A device used to send data entered by a user to a server. Typically, a PC or smartphone is used.

[1719] 3. Server: Receives data sent by users, stores it in a database, and performs analysis. Uses Flask framework to process HTTP requests and Python to analyze data.

[1720] 4. Database: Data storage for saving user submitted opinions and complaints. A relational database such as MySQL is used.

[1721] 5. Generative AI model: An artificial intelligence model that allows the server to send prompts based on the analysis results and generate advice for business improvement.

[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 examples

[1729] Consider the case where a user wants to express their dissatisfaction by saying, "The meeting is too long." The user enters "The meeting is too long" into the text box on the web browser and clicks the send button. This action sends the user's dissatisfaction from the terminal to the server.

[1730] The server receives HTTP POST requests using the Flask framework, parses the received data, and stores it in a MySQL database, for example by executing the following SQL query: INSERT INTO complaints (text, timestamp) VALUES ('Meetings take too long', '2023-10-01 12:00:00').

[1731] The server then uses Python's pandas library to read the complaints data from the database and perform analysis, for example, calculating the frequency of certain keywords to understand which types of complaints are most common.

[1732] Finally, the server sends a prompt to the generative AI model based on the analysis results. For example, in response to a complaint about "meetings being too long," the server sends a prompt such as "We received a complaint that the meetings were too long. Please generate business improvement advice for this complaint." to the generative AI model. The generative AI model generates business improvement advice based on this prompt and returns it to the server. The server 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 improving business operations. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1734] Step 1:

[1735] The user enters a complaint.

[1736] A user accesses the system's interface using a web browser. The interface displays a text box for entering work-related complaints and a submit button. The user enters the complaint in the text box and clicks the submit button. The input data is the complaint text entered by the user. The output data is the event of clicking the submit button.

[1737] Step 2:

[1738] The terminal transmits the inputted 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 complaint text entered by the user. The input data is the complaint text 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 stores it in a database.

[1742] The server receives HTTP POST requests using the Flask framework. It parses the received complaint text and stores it in a MySQL database by adding a new record to a database table, recording the complaint text and a timestamp. The input data is the complaint text included in the HTTP POST request. The output data is the new record stored in the database.

[1743] Step 4:

[1744] The server analyzes the stored complaints.

[1745] The server periodically analyzes the complaints stored in the database. The analysis is performed using the Python pandas library. For example, the frequency of occurrence of specific keywords is calculated to determine which types of complaints are most common. The input data is the text data of complaints obtained from the database. The output data is the analysis results, including the frequency of occurrence of keywords and trends in complaints.

[1746] Step 5:

[1747] The server sends prompts to the generative AI model, which generates advice for business improvement.

[1748] The server sends a prompt to the generative AI model based on the analysis results. The prompt includes specific details of the complaint. The generative AI model generates advice for business improvement based on the received prompt and returns it to the server. The server provides this advice to the user. The input data are the analysis results and the generated prompt. The output data is the advice for business improvement returned by the generative AI model.

[1749] (Application example 3)

[1750] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1751] With conventional business improvement systems, it was difficult to efficiently collect and analyze opinions and complaints from employees. Furthermore, even when there was no time to provide advice on business improvement, there was a lack of means to appropriately handle employee complaints. This meant that feedback for business improvement was not fully utilized, hindering improvements in business efficiency.

[1752] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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 saving the input complaints in a database, means for inputting the saved complaints into a generative AI model and generating analysis results, and means for notifying a manager of the analysis results. This makes it possible to efficiently collect complaints from employees and analyze them using the generative AI model, thereby providing quick feedback to the manager and enabling them to take concrete action to improve business operations.

[1754] A "means for collecting opinions on business improvement" is a method or device for collecting opinions on business improvement from employees.

[1755] "Means for collecting complaints as a countermeasure when no suggestions for improving work are given" refers to a method or device for collecting complaints when no suggestions for improving work are given from employees.

[1756] The "means for collecting complaints as a measure when there is no time to give advice on business improvement" refers to a method or device for collecting complaints when there is insufficient time to give advice on business improvement.

[1757] The "means for providing an interface for inputting complaints" refers to a method or device that provides a user interface for employees to input complaints.

[1758] The "means for storing input complaints in a database" refers to a method or device for storing complaints input by employees in a database.

[1759] "Means for inputting stored complaints into a generative AI model and generating analysis results" refers to a method or device for inputting complaints stored in a database into a generative AI model and generating analysis results.

[1760] "Means for notifying the administrator of the analysis results" refers to a method or device for notifying the administrator of the analysis results generated by the generative AI model.

[1761] A system for implementing this invention is configured as an application installed on robots working in factories. This system efficiently collects complaints from employees, analyzes them using a generative AI model, and notifies managers of the results, thereby promoting business improvement.

[1762] Hardware and software used

[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 complaint input interface:

[1767] The server runs a web server using Flask and serves a web page on a touchscreen display with a text box and a submit button, allowing employees to enter their complaints using this interface.

[1768] 2. Complaint Data Collection:

[1769] When a user enters their complaint into the text box and clicks the submit button, the server receives the data and stores it in a SQLite database.

[1770] 3. Dissatisfaction analysis:

[1771] The server periodically retrieves complaint data from the SQLite database and inputs it as prompt sentences to OpenAI GPT-3, and the generative AI model generates analysis results based on the input complaints.

[1772] 4. Reporting to the Administrator:

[1773] The server notifies the administrator of the generated analysis results by email, using the Python smtplib library to send the email.

[1774] Specific examples

[1775] For example, a factory worker types "machine maintenance is overdue" and clicks 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 complaints: Machine maintenance is delayed

[1777] Prompt generative AI model: Generate improvement suggestions for this complaint.

[1778] The generative AI model generates analysis results such as "The maintenance schedule needs to be reviewed," and the server notifies the administrator of the results by email.

[1779] In this way, employee complaints can be efficiently collected and analyzed using generative AI models, providing quick feedback to managers and enabling them to take concrete actions to improve operations.

[1780] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1781] Step 1:

[1782] The server runs a web server using Flask and serves a web page with a text box and a submit button on a touchscreen display. The user uses this interface to input their complaints. The input is the complaint that the user entered in the text box, and the output is the complaint data that is sent to the server when the submit button is clicked.

[1783] Step 2:

[1784] The server receives the complaint data entered when the user clicks the submit button. The input is the complaint data sent by the user, and the output is to save the 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. As input, the complaint data stored in the database is used, and as output, the retrieved complaint data is input to the generative AI model. Specifically, the server executes an SQL query to retrieve complaint data from the database.

[1787] Step 4:

[1788] The server inputs the acquired dissatisfaction data into the generative AI model to generate analysis results. The inputs are the acquired dissatisfaction data and prompt sentences, 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 dissatisfaction data as prompt sentences.

[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 to be sent to the administrator. Specifically, the server uses Python's smtplib library to send an email containing the analysis results to the administrator.

[1791] In this way, complaints from employees can be efficiently collected, analyzed using a generative AI model, and the results communicated to managers, enabling them to take concrete action to improve operations.

[1792] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1793] "Example 1"

[1794] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine analyzes emotions from opinions and complaints entered by users. Specifically, when a user enters an opinion for business improvement,

[1795] When a user enters a comment, the emotion engine analyzes the emotional components of that comment. For example, if a user enters the comment "This task takes too long," the emotion engine analyzes the user's feelings of dissatisfaction and stress from that comment. The results of this analysis are used as reference information when proposing business improvements.

[1796] "Example 2"

[1797] 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 changes in the user's emotions from those inputs. For example, the emotion engine tracks changes in emotions, such as when a user feels dissatisfied when inputting their opinion for the first time, but after inputting several times, they start to feel satisfied. The results of tracking changes in emotions are used as reference information when updating proposals for business improvement. "Example 3"

[1798] Furthermore, the emotion engine takes the user's emotions into consideration when proposing business improvements. Specifically, the emotion engine makes business improvement proposals based on the results of analyzing the user's emotions and tracking changes in emotions. For example, for a task that the user is dissatisfied with, the emotion engine makes proposals to resolve that dissatisfaction. Also, for a task that the user is satisfied with, the emotion engine makes proposals to maintain that satisfaction. In this way, the emotion engine makes it possible to propose business improvements that take the user's emotions into consideration.

[1799] The processing flow of each embodiment will be described below.

[1800] "Example 1"

[1801] Step 1: The user inputs their ideas for business improvement.

[1802] Step 2: The emotion engine analyzes the emotional components from the user's input.

[1803] Step 3: Make business improvement proposals based on the analysis results.

[1804] "Example 2"

[1805] Step 1: The user enters their opinion or complaint multiple times.

[1806] Step 2: The emotion engine tracks the emotional changes from the user's input.

[1807] Step 3: Update business improvement proposals based on the results of tracking changes in sentiment.

[1808] "Example 3"

[1809] Step 1: The emotion engine proposes business improvements based on the results of analyzing the user's emotions and tracking changes in emotions.

[1810] Step 2: For tasks that users are dissatisfied with, make suggestions to resolve the dissatisfaction.

[1811] Step 3: For tasks that users find satisfying, suggestions are made to maintain that satisfaction.

[1812] Example 1

[1813] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1814] Conventional business improvement systems lack the means to effectively collect and analyze user opinions and complaints, which means that business improvement proposals are not made appropriately. In addition, because analysis does not take into account user emotions, it is difficult to propose improvements that reflect the user's true intentions and emotions.

[1815] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1816] In this invention, the server includes means for collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for receiving opinions entered by users, means for sending the received opinions to a sentiment analysis engine, means for the sentiment analysis engine to analyze the opinions and extract emotional elements, means for saving the analysis results, and means for proposing business improvement based on the analysis results. This makes it possible to effectively collect user opinions and complaints and, through sentiment analysis, to propose business improvement that reflects the user's true intentions and emotions.

[1817] The "means for collecting opinions on business improvement" is a function that provides an interface for users to input improvement suggestions for business operations and send them to the system.

[1818] The "means for collecting complaints" is a function that provides an interface for users to input complaints or problems about work and send them to the system.

[1819] The "means for receiving opinions input by users" is a function by which the server receives opinions sent by users via the web interface.

[1820] The "means for transmitting received opinions to a sentiment analysis engine" is a function for transmitting user opinions received by the server to a sentiment analysis engine.

[1821] "Means for the emotion analysis engine to analyze opinions and extract emotional elements" is a function in which the emotion analysis engine analyzes user opinions and extracts emotional elements from them.

[1822] "Means for saving analysis results" refers to a function for saving the analysis results obtained from the emotion analysis engine in a database.

[1823] "Means for proposing business improvements based on analysis results" is a function that generates proposals for business improvements based on saved analysis results and notifies managers and related parties.

[1824] MODE FOR CARRYING OUT THE INVENTION

[1825] The present invention is a system for effectively collecting and analyzing opinions on business improvement. A specific embodiment of this system will be described below.

[1826] System configuration

[1827] This system consists of three main elements: a server, terminals, and users. The server is responsible for collecting opinions on business improvements, analyzing the received opinions, saving the analysis results, and proposing business improvements. The terminals provide an interface for users to input their opinions. Users input their opinions through the terminals and send them to the system.

[1828] Hardware and software used

[1829] The server uses a general web server and implements a sentiment analysis engine using NLTK, a Python natural language processing library, and TextBlob, a sentiment analysis library. The database uses MySQL and the web framework Flask. The terminal uses a web browser (e.g., Google Chrome) for user access.

[1830] System Operation

[1831] A user accesses the system's web interface using a web browser. The user enters their opinion in a text box for entering opinions or complaints about business improvement and clicks the "Submit" button. For example, the user may enter "This task takes too long."

[1832] The server receives the opinions sent by the user as an HTTP request. The server analyzes this request and extracts the text data of the opinions. The extracted text data of the opinions is sent to the sentiment analysis engine. The sentiment analysis engine uses NLTK and TextBlob to analyze the opinions 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 by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The server then makes business improvement proposals based on the stored analysis results. For example, if multiple users have similar complaints, the server generates proposals for reviewing the business process and notifies the administrator.

[1834] Examples of concrete examples and prompts

[1835] For example, a user accesses a web interface and types, "This task takes too long." The server receives this comment and sends it to a sentiment analysis engine. The sentiment analysis engine uses TextBlob to analyze emotions such as "dissatisfaction" or "stress" and returns the results to the server. The server stores the analysis results in a database and uses them later to make suggestions for improving the task.

[1836] Example prompt sentence:

[1837] "A user has entered an idea for improving business operations. Please use the sentiment engine to analyze the sentiment of this idea and save the results."

[1838] In this way, a system is realized in which the server, terminals, and users each play their respective roles, effectively collecting and analyzing opinions for business improvement.

[1839] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1840] Step 1:

[1841] User enters their opinion

[1842] A user accesses the system's web interface using a web browser. The user enters their opinion or complaint about business improvement in the text box and clicks 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 opinion

[1845] The server receives user-submitted opinions as HTTP POST requests. The server uses the Flask framework to parse the requests and extract the opinion text data. The input data is the HTTP POST request, and the output is the extracted opinion text data.

[1846] Step 3:

[1847] The server sends the opinion to the sentiment analysis engine

[1848] The server sends the extracted text data of opinions to a 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 text data of opinions, and the output is the data sent to the sentiment analysis engine.

[1849] Step 4:

[1850] Sentiment analysis engine analyzes opinions

[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 the opinion "This task takes too much time." The input data is the text data of the opinions, and the output is the results of the sentiment analysis.

[1852] Step 5:

[1853] The server stores the analysis results

[1854] The server receives the analysis results returned by the sentiment analysis engine and stores them in a MySQL database. The stored data includes user opinions and their sentiment analysis results. The input data is the sentiment analysis results, and the output is the data stored in the database.

[1855] Step 6:

[1856] The server makes suggestions for business improvement based on the analysis results

[1857] The server then makes business improvement proposals based on the saved analysis results. For example, if multiple users have similar complaints, it generates proposals for reviewing the business process and notifies the administrator. The input data are the analysis results saved in the database, and the output is the generated business improvement proposals.

[1858] (Application example 1)

[1859] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1860] Conventional business improvement systems lacked the means to efficiently collect and analyze opinions and complaints from workers. They also lacked the functionality to analyze the emotional components of collected opinions and complaints and provide feedback to managers in real time, making it difficult to quickly improve business operations.

[1861] The identification process by the identification 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 collecting opinions on business improvement, means for collecting complaints as a countermeasure when no opinions on business improvement are provided, means for collecting complaints as a countermeasure when there is no time to provide advice on business improvement, means for analyzing the emotional elements of the 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] The "means for collecting opinions on work improvement" is a function that provides an interface for workers to input improvement suggestions regarding work.

[1863] The "means of collecting complaints as a countermeasure when no suggestions for work improvement are made" is a function that provides an interface for workers to input complaints about their work.

[1864] The "means of collecting complaints as a countermeasure when there is not enough time to give advice on business improvement" is a function that provides an interface for collecting complaints from workers when there is not enough time to give advice on business improvement.

[1865] "Means for analyzing the emotional components of opinions entered using an emotion analysis engine" means software or algorithms for analyzing the emotional components of opinions or complaints entered by workers.

[1866] "Means for notifying analysis results in real time" is a function for immediately notifying factory managers of the results of analysis by the emotion analysis engine.

[1867] "Means for saving analysis results" refers to a function for saving the results of analysis by the emotion analysis engine in a database or the like.

[1868] As an embodiment of the present invention, a system is provided that collects opinions on work improvement and complaints from workers in a factory, analyzes the 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] Device: A smartphone or tablet where workers can input their opinions and complaints.

[1873] Network: The Internet or local network for connecting the server and devices.

[1874] software

[1875] Web-based interface: A user interface for workers to input their opinions and complaints.

[1876] Sentiment analysis engine: Software that uses NLTK (Natural Language Toolkit) to analyze the emotional components of input opinions.

[1877] Database: A database using SQLite to store collected opinions and analysis results.

[1878] Processing flow

[1879] 1. Feedback collection: Workers use their smartphones or tablets to access a web-based interface and enter their feedback and complaints about work improvements.

[1880] 2. Data transmission: The entered opinions and complaints are transmitted to the server via the network.

[1881] 3. Sentiment analysis: The sentiment analysis engine on the server analyzes the emotional components 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: The analysis results, as well as the entered opinions and complaints, are stored in a database.

[1884] Specific examples

[1885] For example, if a worker inputs an opinion such as, "This machine breaks down frequently, causing work delays," the emotion analysis engine will extract emotions such as "dissatisfaction" and "stress." The results of this analysis are immediately notified to the factory manager and saved in a database.

[1886] Prompt Sentence Examples

[1887] Analyze the opinions entered by workers using a sentiment analysis engine to extract emotional elements. For example, extract emotions such as "dissatisfaction" and "stress" from the opinion, "This machine breaks down frequently, causing work delays."

[1888] In this way, it is possible to efficiently collect opinions and complaints from workers, analyze their emotional factors, and provide feedback to managers in real time, which is expected to lead to rapid improvements in operations.

[1889] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1890] Step 1:

[1891] Users access a web-based interface using a smartphone or tablet and enter their opinions on business improvements or complaints. The entered data is saved in text format on the device.

[1892] Step 2:

[1893] The terminal transmits the inputted opinions and complaints to the server via the network. The transmitted data is in text format.

[1894] Step 3:

[1895] The server passes the received opinions and complaints to a sentiment analysis engine, which uses the Natural Language Toolkit (NLTK) to analyze the emotional components of the text data. Specifically, it tokenizes the text data and calculates a sentiment score for each...

Claims

1. A means for receiving opinions or complaints regarding the operation of the machine or the working environment inputted by each worker working in the factory; A means for recognizing a score indicating the emotion of the worker from the inputted opinion or dissatisfaction using an emotion engine; A prompt sentence instructing to generate an improvement plan for the machine or the work environment based on the inputted opinion or dissatisfaction and the score of each worker, and a generating system AI is used to generate an improvement plan for the machine or the work environment; A system including:

2. and for each worker, means for tracking changes in said worker's score; The system described in claim 1, wherein the means for generating improvement proposals generates improvement proposals for the machine or work environment using a prompt sentence instructing the generation of improvement proposals for the machine or work environment based on the input opinions or complaints and changes in the scores of each worker, and the generative AI.

3. 2. The system of claim 1, further comprising means installed on a machine in a factory for providing an interface for said workers to input said opinions or complaints.

Images