system

Abstract

We provide the system. [Solution] A means for users to input or upload work details, A means for analyzing the data received by the server, A means by which the server automatically generates a report based on the analysis results, The means by which the terminal presents the report to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, many workers perform various tasks and need to accurately grasp the results for the organization or their own career development. However, the process of appropriately organizing the work content and results and obtaining feedback is often time-consuming and incomplete. As a result, workers cannot grasp their own strengths and areas for improvement, and there is a problem that it is difficult to take appropriate actions for career development and skill improvement.

Means for Solving the Problems

[0005] This invention relates to a system in which a user inputs or uploads their work details, a server analyzes the received data, automatically generates a report based on the analysis results, and presents it to the user via a terminal. This system includes a function in which the server uses natural language processing technology to analyze work details data in detail, and generates a report that includes overall work performance, skill analysis, and improvement suggestions. This allows users to clearly understand their own work content and results, and use this information to build future career plans and improve their skills.

[0006] A "user" is an individual or group that uses this system and is the entity that inputs or uploads work details.

[0007] A "server" is a computer system that receives data sent by users, performs analysis, and generates reports based on the analysis results.

[0008] A "terminal" is a device used by a user to access the system, input and upload data, and view generated reports.

[0009] "Job description" refers to data including details of the tasks performed by the user, project history, and results.

[0010] "Input" refers to the act of a user manually filling in details of their work in a form or interface within a system.

[0011] "Uploading" refers to the act of a user sending existing business data files to the system.

[0012] "Analysis" refers to the process by which a server processes business data it receives, extracts important information, and categorizes it.

[0013] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract meaning and context from data.

[0014] A "report" is a document generated by the server based on the analysis results, including the user's work performance, skill analysis, and improvement suggestions.

[0015] "Presentation" refers to the act of the terminal showing the generated report to the user.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that supports the inventory of work content. It allows users to organize their own work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server.

[0038] System Configuration

[0039] 1. The user enters and uploads the details of their work:

[0040] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[0041] The terminal receives input data and files from the user and first checks their format and content.

[0042] 2. The server analyzes the data:

[0043] The device sends the data it receives to the server.

[0044] The server passes the received data to an analysis module, which then uses natural language processing technology to analyze the business content data.

[0045] This analysis allows the server to extract data points such as the type of work, project size, achievement level, and skills used.

[0046] 3. The server automatically generates reports:

[0047] Based on the analysis results, the server organizes the user's work history and creates reports categorized into, for example, "overall work performance," "skill analysis," and "improvement suggestions."

[0048] The reports will be generated in PDF or HTML format, making them easy for users to review.

[0049] 4. The device presents the report to the user:

[0050] The server sends the generated report to the terminal.

[0051] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill evaluations, and areas for improvement.

[0052] Specific example

[0053] Example 1: User A, a sales professional

[0054] 1. Login and data entry / upload:

[0055] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[0056] 2. Data Analysis:

[0057] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them.

[0058] 3. Report generation:

[0059] The server generates reports based on user A's work performance, categorized as "annual sales achievement rate," "customer satisfaction," and "sales area."

[0060] 4. Report submission:

[0061] The terminal displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement and develop a plan of action for the future.

[0062] Example 2: In the case of user B, an engineer.

[0063] 1. Login and data entry / upload:

[0064] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0065] 2. Data Analysis:

[0066] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate.

[0067] 3. Report generation:

[0068] The server generates a report that includes User B's skill matrix, case studies of successful projects, and suggestions for future skill development.

[0069] 4. Report submission:

[0070] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[0071] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback, thereby contributing to the development of career plans and the improvement of skills.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[0075] Step 2:

[0076] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[0077] Step 3:

[0078] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[0079] Step 4:

[0080] The server analyzes the results and generates a report summarizing the user's work history. The report includes categories such as "Overall Work Performance," "Skill Analysis," and "Improvement Suggestions." For example, it details the number of projects completed annually, success rate, areas of expertise, and skill areas that need further development. The server saves the generated report for each user and allows output in PDF or HTML format as needed.

[0081] Step 5:

[0082] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill evaluation, and areas for improvement. For example, they may see specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved."

[0083] (Example 1)

[0084] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Traditional work inventory systems struggled to efficiently analyze user-entered data and provide detailed feedback. Furthermore, insufficient checks on data format and content could lead to erroneous analysis results. Additionally, the cumbersome report generation and display process made it difficult for users to quickly grasp their own work performance and skill assessments.

[0086] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0087] In this invention, the server includes means for the user to input or upload work details, means for the terminal to check the format and content of the data received from the user, means for the terminal to send the verified data to the server, means for the server to analyze the received data, means for the server to create reports for each category based on the analysis results, means for the server to generate the reports in PDF or HTML format, and means for the terminal to display the generated reports on the user interface. This makes it possible to efficiently analyze the user's work details and provide accurate feedback. In addition, since the format and content of the data are checked, the reliability of the analysis results is improved, and the user can quickly grasp their work performance and skill evaluation.

[0088] A "user" is an individual or legal entity that uses the system and inputs or uploads information about their work or project history.

[0089] A "terminal" is a device used by users to input or upload work details and project history, and it is a device that checks the format and content of the data and communicates data with the server.

[0090] A "server" is a computer system that analyzes data received from terminals and generates reports based on the results.

[0091] An "input form" is an online data entry interface that allows users to manually enter details of their work and project history.

[0092] "Uploading" refers to the act of a user transferring data files, including work details and project history, to a system via their device.

[0093] "Checking data format and content" is a verification process in which the terminal checks the integrity and format of the data received from the user and sends it to the server in a state suitable for analysis.

[0094] "Analysis" is the process of analyzing work content, skills, and project history based on data received by the server, and extracting important data points.

[0095] "Natural language processing technology" is a computer technology that allows servers to understand and analyze the meaning of text data obtained from users.

[0096] A "category" is a broad classification used to categorize analysis results, and examples include overall business performance, skill analysis, and improvement suggestions.

[0097] A "report" is a document generated by the server based on the analysis results, and it serves as a report for users to review their work performance and skill evaluations.

[0098] "PDF format" is an abbreviation for Portable Document Format, and it is one of the fixed-layout document file formats.

[0099] "HTML format" is an abbreviation for HyperText Markup Language, and it is one of the markup languages ​​used to describe web pages.

[0100] A "user interface" refers to the screens and operational areas on a device that allow the user and system to interact, and is the interface where generated reports are displayed.

[0101] Modes for carrying out the invention

[0102] This invention relates to a system that supports the inventory of work content. The system allows users to organize their work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server. The following describes an embodiment of this system in detail.

[0103] Program Overview

[0104] This system involves a series of processes in which users input or upload work details and project history, a server analyzes that data, and automatically generates and presents reports to the user. Each step is implemented using specific hardware and software.

[0105] 1. User input and upload of work details

[0106] Users log in to the system using their terminals and input their work details and project history, or upload existing data files (e.g., Excel or CSV). This allows users to easily provide data to the system. The input form is an online data entry interface. [Specific example: User A fills in their past sales performance in the input form and uploads an Excel file.]

[0107] 2. Checking the data format and content using the terminal.

[0108] The terminal checks the format and content of the received data and sends the validated data to the server. The "pandas" library is used for data validation. This ensures that data suitable for analysis is delivered to the server. [Specific example: The terminal validates the data format and content of an Excel file to verify its consistency.]

[0109] 3. Data analysis by the server

[0110] The server analyzes the received data and extracts important data points using natural language processing techniques. Libraries such as "spaCy" and "NLTK" are used for natural language processing. Machine learning libraries such as "scikit-learn" and "TENSORFLOW®" are also used to extract data points. [Specific example: The server analyzes important data such as sales achievement rates and project completion rates.]

[0111] 4. Automatic report generation by the server

[0112] The server generates reports based on the analysis results and outputs them in PDF or HTML format. "Jinja2" and "ReportLab" are used for report generation, making it easy for users to review the results. [Specific example: The server creates reports categorized by "Annual Sales Achievement Rate," "Customer Satisfaction," etc., and outputs them as PDFs.]

[0113] 5. Presentation of the report

[0114] The terminal displays the generated report in the user interface. Through this, the user can review their work performance, skill evaluation, and areas for improvement. The interface is provided using the front-end frameworks "React" and "Vue.js". [Specific example: The terminal displays a PDF report, and user A views it to check their performance.]

[0115] Specific example

[0116] Example 1: User A, a sales professional

[0117] 1. Login and data entry / upload:

[0118] User A logs into the system and fills in past sales performance and project management results in an input form or uploads an Excel file.

[0119] 2. Check the format and content of the data:

[0120] The terminal checks the data it receives and verifies its format and content.

[0121] 3. Data Analysis:

[0122] The server analyzes the verified data and extracts data points such as sales achievement rates and customer feedback.

[0123] 4. Report generation:

[0124] Based on the analysis results, the server creates reports categorized by "annual sales achievement rate," "customer satisfaction," etc., and generates them in PDF format.

[0125] 5. Report submission:

[0126] The terminal displays the generated report on the user interface, and user A reviews it.

[0127] Example 2: In the case of user B, an engineer.

[0128] 1. Login and data entry / upload:

[0129] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0130] 2. Check the format and content of the data:

[0131] The terminal checks the format and content of the data it receives and sends the verified data to the server.

[0132] 3. Data Analysis:

[0133] The server analyzes the data and extracts data points such as the development language used, tools, and project success rate.

[0134] 4. Report generation:

[0135] The server generates a report based on the analysis results, which includes a skills matrix, case studies of successful projects, and suggestions for future skill development.

[0136] 5. Report submission:

[0137] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[0138] This system efficiently organizes users' work content and provides accurate feedback, thereby contributing to career planning and skill improvement.

[0139] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0140] Step 1:

[0141] Users enter or upload details of their work.

[0142] Users either enter their work details and project history into an input form on the terminal, or upload existing data files (e.g., Excel or CSV files). Specifically, the user logs into the system and enters the necessary information on the input screen, or drags and drops existing performance data. The entered data or uploaded file is imported into the terminal as input data.

[0143] Input: User-entered work details, project history, or uploaded data files.

[0144] Output: Data imported into the terminal

[0145] Step 2:

[0146] The device checks the format and content of the data it receives from the user.

[0147] The terminal checks the format and content of the received data, verifying its integrity and format. Specifically, it uses the "pandas" library to validate the data. This validation process checks, for example, whether all necessary fields are filled in and whether the data format is correct.

[0148] Input: Data imported from the user

[0149] Output: Data with verified format and content.

[0150] Step 3:

[0151] The device sends verified data to the server.

[0152] The device sends verified data to the server via an HTTP request. This process uses libraries such as "axios" or "requests" to send and receive data. Specifically, the device requests data from the server, and the server receives it.

[0153] Input: Data with validated format and content.

[0154] Output: Data received by the server

[0155] Step 4:

[0156] The server analyzes the data.

[0157] The server passes the received data to an analysis module, which then analyzes the data using natural language processing techniques. Libraries such as "spaCy" and "NLTK" are used for this analysis. The server extracts data points such as the type of work, project size, and achievement level, and performs analysis using libraries such as "scikit-learn" and "TensorFlow".

[0158] Input: Data sent to the server

[0159] Output: Extracted data points

[0160] Step 5:

[0161] The server generates reports categorized by type based on the analysis results.

[0162] Based on the analysis results, the server organizes the data by category (e.g., "Overall Business Performance," "Skill Analysis," "Improvement Suggestions") and creates a report. Template engines and document generation libraries such as "Jinja2" and "ReportLab" are used to generate the report. Specifically, the server organizes the analysis results and groups them into categories.

[0163] Input: Extracted data points

[0164] Output: Reports organized by category

[0165] Step 6:

[0166] The server generates reports in PDF or HTML format.

[0167] The server generates the created report in PDF or HTML format. Libraries such as "ReportLab" are used for PDF generation, and "Jinja2" for HTML generation. Specifically, the server applies the report data to a template and generates a report file in the appropriate format.

[0168] Input: Reports organized by category

[0169] Output: Reports in PDF or HTML format

[0170] Step 7:

[0171] The server sends the generated report to the terminal.

[0172] The server sends the generated report to the terminal. This uses the HTTP protocol and the "axios" or "requests" library. Specifically, the server sends a request to the terminal to send the report file, and the terminal receives it.

[0173] Input: PDF or HTML report

[0174] Output: Report received by the terminal

[0175] Step 8:

[0176] The terminal displays the generated report in the user interface.

[0177] The device displays the report received from the server in the user interface. Frontend frameworks such as "React" or "Vue.js" are used for this. Specifically, the device opens a report display screen and presents it to the user in an easy-to-understand manner.

[0178] Input: Report received by the device

[0179] Output: Report displayed in the user interface

[0180] (Application Example 1)

[0181] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0182] Traditional systems for organizing and analyzing business processes required users to manually input large amounts of data, which often resulted in missing or incorrect information. Furthermore, it was difficult to receive immediate feedback on analysis results, hindering direct improvements in work efficiency and skill development. This limitation was particularly evident in situations requiring real-time business improvement, such as factory operations.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0184] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to automatically generate a report based on the analysis results, means for the terminal to present the report to the user, means for the user to input operation details by voice, means for sending data to a cloud server for real-time analysis, and means for displaying the analysis results on a user interface. This reduces the burden on the user to manually input data and allows them to receive analysis results as real-time feedback. This enables improved work efficiency and rapid problem solving in field operations.

[0185] "Means for users to input or upload work details" refers to methods for users to directly input information about their work or to upload existing data files to the system.

[0186] "Means of analyzing data received by the server" refers to the process by which the server performs analysis based on business data received from the user.

[0187] "A means by which a server automatically generates reports based on analysis results" refers to a method in which a server uses the analyzed data to automatically create reports containing useful information for the user.

[0188] "Means by which a terminal presents a report to a user" refers to a method of displaying the generated report on the user's terminal so that the user can review its contents.

[0189] "A means for users to input their operations by voice" refers to a method for users to record their operations by voice and input them into the system.

[0190] "A method for sending data to a cloud server and analyzing it in real time" refers to a method of sending voice or input data from the user to a cloud server and performing immediate analysis.

[0191] "Means of displaying analysis results on a user interface" refers to a method of displaying the analyzed results on a user interface so that the user can intuitively understand the results.

[0192] This invention is a system that supports the efficiency improvement and suggestions for improvements in factory robot operation tasks. Users input their work details and operation history by voice, the data is analyzed in real time on a cloud server, and the results are displayed on smart glasses. A specific embodiment is shown below.

[0193] System program and processing description

[0194] Voice input

[0195] The user wears smart glasses and inputs operation details and maintenance activities via voice. The speech_recognition library is used for voice input. When the user speaks into the glasses, the voice is converted into text data.

[0196] Data transmission and real-time analysis

[0197] The text data converted from the speech is automatically sent to a cloud server. The cloud server uses natural language processing (NLP) technology to analyze the input data in real time. The analysis includes information such as the equipment used, problems encountered, and operational errors.

[0198] Report generation and display

[0199] The cloud server automatically generates a report based on the analysis results. The report includes an evaluation of the operation and suggestions for improvement. This report is displayed on the smart glasses' screen, allowing the user to view the analysis results in real time.

[0200] Hardware and software used

[0201] Hardware: Smart glasses, cloud server, microphone

[0202] Software: speech_recognition library, Natural Language Processing (NLP) module

[0203] Specific example

[0204] Examples of use by factory employees

[0205] For example, if a factory worker says, "Replace the belt on robot A on line 2 and check for oil leaks," the system records that information. The recorded data is sent to a cloud server in real time, and feedback such as "Belt replacement is correct. Oil leak update needed" is displayed on the smart glasses' screen.

[0206] Example of a prompt

[0207] Examples of prompts used by factory workers to give more specific instructions on what to do are as follows:

[0208] Please analyze the following work procedure: "Replace the belt on robot A in line 2 and check for oil leaks."

[0209] Please output an evaluation of the work performed and suggestions for improvement as part of the analysis results.

[0210] This system improves the efficiency of factory operations and enables rapid problem solving, significantly reducing the effort required for manual data entry and task organization.

[0211] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0212] Step 1:

[0213] The user wears smart glasses and inputs the operation details by voice. The user speaks, "Replace the belt on robot A on line 2 and check for oil leaks." This voice input is converted into text data by the smart glasses' microphone and the speech_recognition library.

[0214] Input: Audio data

[0215] Output: Text data (Example: "Robot A on Line 2: Belt replacement, oil leak check")

[0216] Step 2:

[0217] The device (smart glasses) sends the converted text data to the cloud server. The text data is sent to the cloud server using a secure communication protocol (e.g., HTTPS).

[0218] Input: Text data

[0219] Output: Notification of completion of data transmission to cloud server

[0220] Step 3:

[0221] The server passes the received text data to a natural language processing (NLP) module for real-time analysis. Specifically, the data is processed to extract information on the usage status, problems, and operational errors of various equipment within the text data. Generative AI models are also utilized in the analysis.

[0222] Input: Text data

[0223] Output: Analysis results data (e.g., belt replacement appropriate, oil leak location needs updating)

[0224] Step 4:

[0225] The server automatically generates a report based on the analysis results. The report includes an evaluation of user actions and suggestions for improvement. The report is converted to a standard format (e.g., JSON, PDF).

[0226] Input: Analysis result data

[0227] Output: Generated report (e.g., evaluation and suggestion data in JSON format)

[0228] Step 5:

[0229] The server generates a report and sends it to the device (smart glasses). A secure communication protocol is used to transmit data from the cloud server to the smart glasses.

[0230] Input: Generated report

[0231] Output: Notification that the report has been successfully sent to the smart glasses.

[0232] Step 6:

[0233] The device (smart glasses) displays the received report on the user interface. The user can view the analysis results and improvement suggestions in real time through the smart glasses. Specifically, the smart glasses' display visually highlights the key points of the report.

[0234] Input: Generated report

[0235] Output: Report content displayed on the smart glasses screen (e.g., belt replacement appropriate, oil leak location needs updating)

[0236] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0237] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized by utilizing an emotion engine in addition to data exchange between the user, terminal, and server.

[0238] System Configuration

[0239] 1. The user enters and uploads the details of their work:

[0240] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[0241] The terminal receives input data and files from the user and checks their format and content.

[0242] 2. The server analyzes the data:

[0243] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[0244] The server's natural language processing (NLP) module analyzes the text data and extracts data points such as the type of work, project size, achievement level, and skills used.

[0245] 3. Emotion recognition using an emotion engine:

[0246] During the data analysis process, the server uses an emotion engine to analyze emotional information from user input or uploaded data.

[0247] The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[0248] 4. The server automatically generates reports:

[0249] Based on the analysis results and the emotion evaluation by the emotion engine, the server organizes the user's work history and generates a report.

[0250] The report includes categories such as "Overall Performance," "Skill Analysis," "Sentiment Assessment," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate annually, areas of expertise, and areas for future improvement, as well as the evolution of emotional states.

[0251] The server saves reports generated by the server for each user and allows them to be output in PDF or HTML format as needed.

[0252] 5. The device presents the report to the user:

[0253] The server sends the URL or file of the generated report to the terminal.

[0254] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill assessments, sentiment assessments, and areas for improvement.

[0255] Specific example

[0256] Example 1: User A, a sales professional

[0257] 1. Login and data entry / upload:

[0258] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[0259] 2. Data analysis and sentiment recognition:

[0260] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them. The emotion engine analyzes emotional information from user A's input data and evaluates emotions such as "satisfaction" and "frustration."

[0261] 3. Report generation:

[0262] The server generates reports based on user A's work performance, categorized into "annual sales achievement rate," "customer satisfaction," "sales area," and "emotional evaluation."

[0263] 4. Report submission:

[0264] The device displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement, as well as emotional trends, and to plan future actions.

[0265] Example 2: In the case of user B, an engineer.

[0266] 1. Login and data entry / upload:

[0267] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0268] 2. Data analysis and sentiment recognition:

[0269] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate. The emotion engine analyzes emotional information from user B's input data and evaluates emotions such as "excitement" and "stress."

[0270] 3. Report generation:

[0271] The server generates a report that includes User B's skills matrix, case studies of successful projects, and suggestions for future skill development. The report also includes an emotional assessment, visualizing the ongoing changes in User B's emotional state during work.

[0272] 4. Report submission:

[0273] The device displays the generated report, and by reviewing it, User B can gain a clear understanding of their strengths, the skills they need to learn in the future, and even emotional trends.

[0274] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback and emotional evaluation, thereby contributing to the development of career plans and the improvement of skills.

[0275] The following describes the processing flow.

[0276] Step 1:

[0277] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[0278] Step 2:

[0279] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[0280] Step 3:

[0281] The server passes the received data to the analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management", "sales achievement", and "customer satisfaction". The server classifies the business content by type and calculates data points such as the results, skill sets, and achievement levels for each project.

[0282] Step 4:

[0283] In parallel with the analysis, the server uses the sentiment engine to analyze sentiment information from the user's input data or uploaded data. The sentiment engine analyzes the sentiment tone of the text data (e.g., positive, negative, neutral) and evaluates the user's sentiment state (e.g., satisfied, anxious, excited). This sentiment evaluation is also used as input data when the server generates an automatic report.

[0284] Step 5:

[0285] Based on the analysis results and sentiment evaluation, the server organizes the user's business history and generates an automatic report. The report includes categories such as "overall business performance", "skill analysis", "sentiment evaluation", and "improvement suggestions". For example, in addition to the number of projects achieved annually and the success rate, the skills the user is good at, and the areas for future improvement, the transition of the sentiment state is described in detail. The server saves the report generated for each user and makes it possible to output it in PDF or HTML format as needed.

[0286] Step 6:

[0287] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill assessment, sentiment assessment, and areas for improvement. For example, specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved" is visualized, as well as sentiment assessments such as "Stress levels increased in recent projects."

[0288] This allows users to understand not only their work content and results, but also their emotional trends, which can be used to build career plans and improve their skills.

[0289] (Example 2)

[0290] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0291] Conventional business process inventory systems analyzed business data and generated reports without considering the user's emotional state. As a result, there was a lack of feedback and improvement suggestions based on user emotions, making it difficult to improve user satisfaction and engagement. Furthermore, insufficient data formatting checks and the use of emotion recognition technology made it difficult to generate accurate and useful reports.

[0292] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input or upload work content, means for the terminal to check the format and content of the data, means for the terminal to transmit the checked data to the server, means for the server to analyze the received data, means for the server to analyze emotional information using emotional recognition technology, means for the server to generate an automatic report based on the analysis results and emotional evaluation, and means for the terminal to present the report to the user. This makes it possible to provide feedback and improvement suggestions based on emotional information in addition to the user's work content.

[0293] A "user" refers to a person who inputs or uploads data such as work details and project history into the system.

[0294] A "terminal" refers to a device or software that receives input data and files from a user, checks their format and content, and sends the data to a server.

[0295] A "server" refers to a computer system that analyzes received data, performs emotional analysis, and automatically generates reports based on the analysis results.

[0296] "Data format" refers to the file format of the data entered or uploaded by the user, such as CSV or Excel files.

[0297] "Data content" refers to specific information about work details and project history included in the data entered or uploaded by the user.

[0298] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords and numerical information.

[0299] "Emotion recognition technology" refers to technology that analyzes emotional tone from text data and evaluates the user's emotional state.

[0300] An "automated report" refers to a report generated based on analysis results and sentiment evaluations, and includes categories such as work performance, skill analysis, sentiment evaluation, and improvement suggestions.

[0301] An "input form" refers to an online form used by users to input details about their work or project history.

[0302] An "analysis module" refers to a software component used by a server to analyze the data it receives.

[0303] "Encryption" refers to the technology of encoding information to securely transmit data.

[0304] "Emotional tone" refers to emotional nuances such as positive, negative, and neutral contained in text data.

[0305] "Feedback" refers to timely and appropriate evaluations and advice provided to the user based on analysis results and emotional evaluations.

[0306] This invention relates to a system that supports inventorying business content, recognizes the user's emotions, and provides feedback based on the analysis results. In this system, the user, terminal, and server exchange data, and it is realized by using an emotion engine.

[0307] Main components of the system

[0308] 1. The user inputs and uploads business content:

[0309] The user inputs their business content and project history into an input form or uploads an existing data file (e.g., Excel or CSV file). The terminal receives the input data and file from the user and checks its format and content.

[0310] 2. The terminal checks the data:

[0311] The terminal checks the format and content of the data received from the user. If the data format is inappropriate or required items are missing, an error message is displayed to the user.

[0312] 3. The terminal sends the data to the server:

[0313] The terminal sends the checked data to the server. When sending, the data is encrypted to ensure secure communication.

[0314] Hardware and software used

[0315] Hardware: Devices (computers, smartphones, etc.), servers

[0316] software:

[0317] Web form and file upload functionality

[0318] Natural Language Processing (NLP) modules (e.g., SpaCy, NLTK)

[0319] Sentiment recognition engines (e.g., IBM Watson®, Microsoft® Azure® Text Analytics)

[0320] Report generation modules (e.g., JasperReports, Apache® FOP)

[0321] HTTPS protocol for encrypted and secure communication

[0322] Specific example

[0323] Example 1: User A, a sales professional

[0324] 1. The user inputs and uploads their work details: User A logs into the system and uploads a CSV file containing sales performance data for the past year.

[0325] 2. The terminal checks the data: The terminal checks the format of the CSV data and verifies that all necessary items are included.

[0326] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[0327] Example 2: In the case of user B, an engineer.

[0328] 1. The user inputs and uploads their work details: User B logs into the system and uploads a CSV file containing their development project history and skill set.

[0329] 2. The terminal checks the data: The terminal checks the format and content of the CSV data and verifies that there are no missing items.

[0330] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[0331] Example of a prompt

[0332] "I have entered and uploaded sales performance data for the past year. Please analyze this data and generate a report evaluating sales achievement rate, customer satisfaction, and sentiment tone."

[0333] In this way, we aim to contribute to the development of users' career plans and skill improvement by comprehensively managing users' work content and emotional evaluations, and providing more accurate feedback and improvement suggestions.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] Users input or upload details of their work.

[0337] Specific actions:

[0338] Users log in to the system and enter their work details and project history into an input form. Alternatively, they can upload existing data files (e.g., Excel or CSV files).

[0339] input:

[0340] Text data entered by the user or a CSV file uploaded by the user.

[0341] output:

[0342] The input data file transferred to the terminal.

[0343] Step 2:

[0344] The device checks the data format and content.

[0345] Specific actions:

[0346] The terminal checks the file format of the entered or uploaded data (whether it's CSV, Excel, etc.) and verifies that the content itself contains all required fields (e.g., project name, start date, end date). If there are any deficiencies in the format or content, an error message is displayed to the user.

[0347] input:

[0348] Data files received from the user.

[0349] output:

[0350] If the data format and content are correct, it will be marked as checked data. If there are errors in the format or content, an error message will be displayed.

[0351] Step 3:

[0352] The terminal sends the checked data to the server.

[0353] Specific actions:

[0354] The terminal encrypts the checked data and sends it to the server using a secure communication protocol (HTTPS). This ensures the security of the data.

[0355] input:

[0356] Checked data.

[0357] output:

[0358] The encrypted data is sent to the server.

[0359] Step 4:

[0360] The server analyzes the data it receives.

[0361] Specific actions:

[0362] The server receives the data and passes it to an analysis module (e.g., SpaCy, NLTK). The NLP module analyzes the data and extracts data points such as the type of work, project size, achievement level, and skills used.

[0363] input:

[0364] Encrypted data.

[0365] output:

[0366] Analyzed data points (e.g., type of work, project size, achievement level, skills used).

[0367] Step 5:

[0368] The server analyzes emotional information using emotion recognition technology.

[0369] Specific actions:

[0370] The server passes data acquired from the analysis module to an emotion recognition engine (e.g., IBM Watson, Microsoft Azure Text Analytics). The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[0371] input:

[0372] Analyzed data points.

[0373] output:

[0374] Results of the assessment of emotional tone and emotional state.

[0375] Step 6:

[0376] The server automatically generates a report based on the analysis results and sentiment evaluation.

[0377] Specific actions:

[0378] The server launches a report generation module (e.g., JasperReports) and creates a report based on the analysis results and sentiment evaluation, including categories such as "Overall Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." The report is output in PDF or HTML format.

[0379] input:

[0380] Results of the assessment of emotional tone and emotional state.

[0381] output:

[0382] An automatically generated report (in PDF or HTML format).

[0383] Step 7:

[0384] The server sends the URL or file of the generated report to the terminal.

[0385] Specific actions:

[0386] The server re-encrypts the generated report file and sends it to the terminal via URL or file. HTTPS is used as the communication protocol.

[0387] input:

[0388] An automatically generated report.

[0389] output:

[0390] An encrypted report file is sent to the terminal.

[0391] Step 8:

[0392] The device presents the report to the user.

[0393] Specific actions:

[0394] The terminal decrypts the received report file and displays it on the user interface. Users can then review their work performance, skill evaluations, sentiment evaluations, and improvement suggestions.

[0395] input:

[0396] Encrypted report file.

[0397] output:

[0398] A report displayed in the user interface.

[0399] (Application Example 2)

[0400] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0401] Conventional business process inventory systems failed to take user emotions into account during analysis, making it impossible to understand users' emotional burdens and stress levels. Furthermore, in factory robot maintenance, there was a lack of feedback reflecting the emotional state of technicians, posing challenges to improving maintenance efficiency and technician satisfaction. This invention aims to solve these problems.

[0402] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0403] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to analyze emotional information using an emotion engine, means for generating an automatic report that includes maintenance history and the emotional evaluation of the technician, and means for the terminal to present the report to the user. This enables detailed feedback including emotional evaluation in addition to the user's work details, and in particular, enables the creation of efficient maintenance plans that reflect the emotional state of the technician in the maintenance of factory robots.

[0404] A "user" is an individual or group that uses the system to input work details or upload data.

[0405] "Job description" refers to the detailed information about the daily tasks and projects that users perform.

[0406] "Input" refers to the act of a user manually providing data to a system.

[0407] "Uploading" refers to the act of a user importing a pre-prepared file into the system.

[0408] "Means" refers to the methods or devices used to achieve a specific objective.

[0409] A "server" is a computer system that stores, processes, and analyzes data.

[0410] "Analysis" is the process of breaking down data into smaller parts and understanding and evaluating its contents.

[0411] An "emotion engine" is software or a system used to analyze the emotions behind text data obtained from users.

[0412] "Emotional information" refers to data that indicates the user's emotional state based on text data analyzed by the emotion engine.

[0413] An "automated report" is a report generated by the server based on analysis results and sentiment information.

[0414] "Maintenance history" refers to detailed records of maintenance work and repairs performed on factory robots.

[0415] A "technician" is a specialist who performs maintenance on factory robots.

[0416] "Presentation" refers to the act of showing or providing a generated report to the user.

[0417] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized through data exchange between the user, terminal, and server, as well as the use of an emotion engine.

[0418] System Configuration

[0419] 1. The user enters and uploads the details of their work.

[0420] Users enter their work details and maintenance history into an input form, or upload existing data files (e.g., Excel or CSV files). Users perform these operations after logging into a specific system.

[0421] The terminal receives input data and files from the user and checks their format and content.

[0422] 2. The server analyzes the data.

[0423] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[0424] The server's natural language processing module (e.g., pandas, TextBlob) analyzes the maintenance history and extracts data points such as the type and frequency of work and the number of times parts were replaced. The server then uses an emotion engine to analyze sentiment information from comments written by users.

[0425] 3. Emotion recognition using an emotion engine

[0426] The server analyzes the emotional tone (e.g., positive, negative, neutral) from the text data of maintenance records and evaluates the emotional state of the technicians. The results of the emotion engine's analysis are stored in a database.

[0427] 4. The server automatically generates reports.

[0428] Based on the analysis results and the emotion evaluation by the emotion engine, the server automatically generates a report that includes maintenance history, frequency of parts replacement, and the emotion evaluation of the technicians. The report records in detail the maintenance status of each robot, the emotional state of the technicians, and the degree of wear and tear on the parts.

[0429] The server saves the generated report in PDF or HTML format and sends it to the terminal as needed.

[0430] 5. The device presents the report to the user.

[0431] The terminal displays the URL or file of the report received from the server in the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. Based on this information, future maintenance plans and training programs for technicians are developed.

[0432] Specific example

[0433] For example, if a technician comments, "There are too many parts replacements, and it's stressful," the emotion engine will rate this comment as a negative emotion. The server will then generate an automated report that includes this emotion rating and present the results to the technician and administrator to provide specific improvement suggestions and feedback.

[0434] Example of a prompt

[0435] Examples of prompt statements to input into a generative AI model include the following:

[0436] "Analyze the maintenance history of factory robots and evaluate the sentiment of the technicians. The data will include comments such as:

[0437] 'Too many parts need replacing, it's stressful.'

[0438] 'I was satisfied because the work went smoothly.'

[0439] 'I'm having trouble using the new tool.'

[0440] Based on these comments, please evaluate them as positive, negative, or neutral and reflect this in your report.

[0441] Based on this configuration, the system of the present invention efficiently organizes the user's work content and emotional state, and provides accurate feedback and emotional evaluation, enabling the creation of efficient maintenance plans that take into account the emotional information of technicians, particularly in the maintenance of factory robots.

[0442] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0443] Step 1:

[0444] Users input and upload details of their work.

[0445] Users log in to the system and enter their work details and maintenance history into input forms, or upload existing data files (e.g., Excel or CSV files). Based on the input, the terminal checks the data format and content and saves it in the appropriate format. It verifies whether the input data format is correct and displays an error message if it is in an inappropriate format.

[0446] Step 2:

[0447] The device sends data to the server.

[0448] The terminal sends data entered or uploaded by the user to the server. Before sending data, it checks the data format and converts it to a format that the server can parse. The input data sent by the terminal to the server includes work content data and maintenance history data, while the output is formatted data.

[0449] Step 3:

[0450] The server analyzes the data.

[0451] The server passes the received data to an analysis module. Using a natural language processing module, the maintenance history is analyzed, and data points such as the type and frequency of work and the number of times parts were replaced are extracted. The input is data provided by the user, and the output is the analyzed data points. The server then performs a detailed analysis based on these points.

[0452] Step 4:

[0453] The server uses an emotion engine to analyze emotional information.

[0454] The server uses an emotion engine to analyze emotional information from user input and uploaded data. Specifically, it evaluates the emotional tone (positive, negative, neutral) of text data and analyzes the emotional state of the engineers. The input is text data including user comments, and the output is the analyzed emotional information.

[0455] Step 5:

[0456] The server automatically generates reports.

[0457] The server automatically generates a report based on the analysis results and the sentiment evaluation by the sentiment engine. The report includes maintenance history, frequency of parts replacement, and the technician's sentiment evaluation. The input is the analysis results up to the previous step, and the output is a document formatted as a report. This ensures that the maintenance status of the robot, the sentiment state of the technician, and the degree of wear and tear on the parts are recorded in detail.

[0458] Step 6:

[0459] The device presents the report to the user.

[0460] The terminal displays the URL or file of the report received from the server on the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. The input is the URL or file of the generated report, and the output is displayed on the user interface. Based on this information, users can develop future maintenance plans and training programs for technicians.

[0461] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0462] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0464] [Second Embodiment]

[0465] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0466] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0467] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0469] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0471] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0472] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0473] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0474] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0476] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0477] This invention is a system that supports the inventory of work content. It allows users to organize their own work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server.

[0478] System Configuration

[0479] 1. The user enters and uploads the details of their work:

[0480] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[0481] The terminal receives input data and files from the user and first checks their format and content.

[0482] 2. The server analyzes the data:

[0483] The device sends the data it receives to the server.

[0484] The server passes the received data to an analysis module, which then uses natural language processing technology to analyze the business content data.

[0485] This analysis allows the server to extract data points such as the type of work, project size, achievement level, and skills used.

[0486] 3. The server automatically generates reports:

[0487] Based on the analysis results, the server organizes the user's work history and creates reports categorized into, for example, "overall work performance," "skill analysis," and "improvement suggestions."

[0488] The reports will be generated in PDF or HTML format, making them easy for users to review.

[0489] 4. The device presents the report to the user:

[0490] The server sends the generated report to the terminal.

[0491] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill evaluations, and areas for improvement.

[0492] Specific example

[0493] Example 1: User A, a sales professional

[0494] 1. Login and data entry / upload:

[0495] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[0496] 2. Data Analysis:

[0497] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them.

[0498] 3. Report generation:

[0499] The server generates reports based on user A's work performance, categorized as "annual sales achievement rate," "customer satisfaction," and "sales area."

[0500] 4. Report submission:

[0501] The terminal displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement and develop a plan of action for the future.

[0502] Example 2: In the case of user B, an engineer.

[0503] 1. Login and data entry / upload:

[0504] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0505] 2. Data Analysis:

[0506] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate.

[0507] 3. Report generation:

[0508] The server generates a report that includes User B's skill matrix, case studies of successful projects, and suggestions for future skill development.

[0509] 4. Report submission:

[0510] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[0511] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback, thereby contributing to the development of career plans and the improvement of skills.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[0515] Step 2:

[0516] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[0517] Step 3:

[0518] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[0519] Step 4:

[0520] The server analyzes the results and generates a report summarizing the user's work history. The report includes categories such as "Overall Work Performance," "Skill Analysis," and "Improvement Suggestions." For example, it details the number of projects completed annually, success rate, areas of expertise, and skill areas that need further development. The server saves the generated report for each user and allows output in PDF or HTML format as needed.

[0521] Step 5:

[0522] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill evaluation, and areas for improvement. For example, they may see specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved."

[0523] (Example 1)

[0524] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0525] Traditional work inventory systems struggled to efficiently analyze user-entered data and provide detailed feedback. Furthermore, insufficient checks on data format and content could lead to erroneous analysis results. Additionally, the cumbersome report generation and display process made it difficult for users to quickly grasp their own work performance and skill assessments.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0527] In this invention, the server includes means for the user to input or upload work details, means for the terminal to check the format and content of the data received from the user, means for the terminal to send the verified data to the server, means for the server to analyze the received data, means for the server to create reports for each category based on the analysis results, means for the server to generate the reports in PDF or HTML format, and means for the terminal to display the generated reports on the user interface. This makes it possible to efficiently analyze the user's work details and provide accurate feedback. In addition, since the format and content of the data are checked, the reliability of the analysis results is improved, and the user can quickly grasp their work performance and skill evaluation.

[0528] A "user" is an individual or legal entity that uses the system and inputs or uploads information about their work or project history.

[0529] A "terminal" is a device used by users to input or upload work details and project history, and it is a device that checks the format and content of the data and communicates data with the server.

[0530] A "server" is a computer system that analyzes data received from terminals and generates reports based on the results.

[0531] An "input form" is an online data entry interface that allows users to manually enter details of their work and project history.

[0532] "Uploading" refers to the act of a user transferring data files, including work details and project history, to a system via their device.

[0533] "Checking data format and content" is a verification process in which the terminal checks the integrity and format of the data received from the user and sends it to the server in a state suitable for analysis.

[0534] "Analysis" is the process of analyzing work content, skills, and project history based on data received by the server, and extracting important data points.

[0535] "Natural language processing technology" is a computer technology that allows servers to understand and analyze the meaning of text data obtained from users.

[0536] A "category" is a broad classification used to categorize analysis results, and examples include overall business performance, skill analysis, and improvement suggestions.

[0537] A "report" is a document generated by the server based on the analysis results, and it serves as a report for users to review their work performance and skill evaluations.

[0538] "PDF format" is an abbreviation for Portable Document Format, and it is one of the fixed-layout document file formats.

[0539] "HTML format" is an abbreviation for HyperText Markup Language, and it is one of the markup languages ​​used to describe web pages.

[0540] A "user interface" refers to the screens and operational areas on a device that allow the user and system to interact, and is the interface where generated reports are displayed.

[0541] Modes for carrying out the invention

[0542] This invention relates to a system that supports the inventory of work content. The system allows users to organize their work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server. The following describes an embodiment of this system in detail.

[0543] Program Overview

[0544] This system involves a series of processes in which users input or upload work details and project history, a server analyzes that data, and automatically generates and presents reports to the user. Each step is implemented using specific hardware and software.

[0545] 1. User input and upload of work details

[0546] Users log in to the system using their terminals and input their work details and project history, or upload existing data files (e.g., Excel or CSV). This allows users to easily provide data to the system. The input form is an online data entry interface. [Specific example: User A fills in their past sales performance in the input form and uploads an Excel file.]

[0547] 2. Checking the data format and content using the terminal.

[0548] The terminal checks the format and content of the received data and sends the validated data to the server. The "pandas" library is used for data validation. This ensures that data suitable for analysis is delivered to the server. [Specific example: The terminal validates the data format and content of an Excel file to verify its consistency.]

[0549] 3. Data analysis by the server

[0550] The server analyzes the received data and extracts important data points using natural language processing techniques. Natural language processing libraries such as "spaCy" and "NLTK" are used. Machine learning libraries such as "scikit-learn" and "TensorFlow" are also used to extract data points. [Specific example: The server analyzes important data such as sales achievement rates and project completion rates.]

[0551] 4. Automatic report generation by the server

[0552] The server generates reports based on the analysis results and outputs them in PDF or HTML format. "Jinja2" and "ReportLab" are used for report generation, making it easy for users to review the results. [Specific example: The server creates reports categorized by "Annual Sales Achievement Rate," "Customer Satisfaction," etc., and outputs them as PDFs.]

[0553] 5. Presentation of the report

[0554] The terminal displays the generated report in the user interface. Through this, the user can review their work performance, skill evaluation, and areas for improvement. The interface is provided using the front-end frameworks "React" and "Vue.js". [Specific example: The terminal displays a PDF report, and user A views it to check their performance.]

[0555] Specific example

[0556] Example 1: User A, a sales professional

[0557] 1. Login and data entry / upload:

[0558] User A logs into the system and fills in past sales performance and project management results in an input form or uploads an Excel file.

[0559] 2. Check the format and content of the data:

[0560] The terminal checks the data it receives and verifies its format and content.

[0561] 3. Data Analysis:

[0562] The server analyzes the verified data and extracts data points such as sales achievement rates and customer feedback.

[0563] 4. Report generation:

[0564] Based on the analysis results, the server creates reports categorized by "annual sales achievement rate," "customer satisfaction," etc., and generates them in PDF format.

[0565] 5. Report submission:

[0566] The terminal displays the generated report on the user interface, and user A reviews it.

[0567] Example 2: In the case of user B, an engineer.

[0568] 1. Login and data entry / upload:

[0569] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0570] 2. Check the format and content of the data:

[0571] The terminal checks the format and content of the data it receives and sends the verified data to the server.

[0572] 3. Data Analysis:

[0573] The server analyzes the data and extracts data points such as the development language used, tools, and project success rate.

[0574] 4. Report generation:

[0575] The server generates a report based on the analysis results, which includes a skills matrix, case studies of successful projects, and suggestions for future skill development.

[0576] 5. Report submission:

[0577] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[0578] This system efficiently organizes users' work content and provides accurate feedback, thereby contributing to career planning and skill improvement.

[0579] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0580] Step 1:

[0581] Users enter or upload details of their work.

[0582] Users either enter their work details and project history into an input form on the terminal, or upload existing data files (e.g., Excel or CSV files). Specifically, the user logs into the system and enters the necessary information on the input screen, or drags and drops existing performance data. The entered data or uploaded file is imported into the terminal as input data.

[0583] Input: User-entered work details, project history, or uploaded data files.

[0584] Output: Data imported into the terminal

[0585] Step 2:

[0586] The device checks the format and content of the data it receives from the user.

[0587] The terminal checks the format and content of the received data, verifying its integrity and format. Specifically, it uses the "pandas" library to validate the data. This validation process checks, for example, whether all necessary fields are filled in and whether the data format is correct.

[0588] Input: Data imported from the user

[0589] Output: Data with verified format and content.

[0590] Step 3:

[0591] The device sends verified data to the server.

[0592] The device sends verified data to the server via an HTTP request. This process uses libraries such as "axios" or "requests" to send and receive data. Specifically, the device requests data from the server, and the server receives it.

[0593] Input: Data with validated format and content.

[0594] Output: Data received by the server

[0595] Step 4:

[0596] The server analyzes the data.

[0597] The server passes the received data to an analysis module, which then analyzes the data using natural language processing techniques. Libraries such as "spaCy" and "NLTK" are used for this analysis. The server extracts data points such as the type of work, project size, and achievement level, and performs analysis using libraries such as "scikit-learn" and "TensorFlow".

[0598] Input: Data sent to the server

[0599] Output: Extracted data points

[0600] Step 5:

[0601] The server generates reports categorized by type based on the analysis results.

[0602] Based on the analysis results, the server organizes the data by category (e.g., "Overall Business Performance," "Skill Analysis," "Improvement Suggestions") and creates a report. Template engines and document generation libraries such as "Jinja2" and "ReportLab" are used to generate the report. Specifically, the server organizes the analysis results and groups them into categories.

[0603] Input: Extracted data points

[0604] Output: Reports organized by category

[0605] Step 6:

[0606] The server generates reports in PDF or HTML format.

[0607] The server generates the created report in PDF or HTML format. Libraries such as "ReportLab" are used for PDF generation, and "Jinja2" for HTML generation. Specifically, the server applies the report data to a template and generates a report file in the appropriate format.

[0608] Input: Reports organized by category

[0609] Output: Reports in PDF or HTML format

[0610] Step 7:

[0611] The server sends the generated report to the terminal.

[0612] The server sends the generated report to the terminal. This uses the HTTP protocol and the "axios" or "requests" library. Specifically, the server sends a request to the terminal to send the report file, and the terminal receives it.

[0613] Input: PDF or HTML report

[0614] Output: Report received by the terminal

[0615] Step 8:

[0616] The terminal displays the generated report in the user interface.

[0617] The device displays the report received from the server in the user interface. Frontend frameworks such as "React" or "Vue.js" are used for this. Specifically, the device opens a report display screen and presents it to the user in an easy-to-understand manner.

[0618] Input: Report received by the device

[0619] Output: Report displayed in the user interface

[0620] (Application Example 1)

[0621] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0622] Traditional systems for organizing and analyzing business processes required users to manually input large amounts of data, which often resulted in missing or incorrect information. Furthermore, it was difficult to receive immediate feedback on analysis results, hindering direct improvements in work efficiency and skill development. This limitation was particularly evident in situations requiring real-time business improvement, such as factory operations.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0624] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to automatically generate a report based on the analysis results, means for the terminal to present the report to the user, means for the user to input operation details by voice, means for sending data to a cloud server for real-time analysis, and means for displaying the analysis results on a user interface. This reduces the burden on the user to manually input data and allows them to receive analysis results as real-time feedback. This enables improved work efficiency and rapid problem solving in field operations.

[0625] "Means for users to input or upload work details" refers to methods for users to directly input information about their work or to upload existing data files to the system.

[0626] "Means of analyzing data received by the server" refers to the process by which the server performs analysis based on business data received from the user.

[0627] "A means by which a server automatically generates reports based on analysis results" refers to a method in which a server uses the analyzed data to automatically create reports containing useful information for the user.

[0628] "Means by which a terminal presents a report to a user" refers to a method of displaying the generated report on the user's terminal so that the user can review its contents.

[0629] "A means for users to input their operations by voice" refers to a method for users to record their operations by voice and input them into the system.

[0630] "A method for sending data to a cloud server and analyzing it in real time" refers to a method of sending voice or input data from the user to a cloud server and performing immediate analysis.

[0631] "Means of displaying analysis results on a user interface" refers to a method of displaying the analyzed results on a user interface so that the user can intuitively understand the results.

[0632] This invention is a system that supports the efficiency improvement and suggestions for improvements in factory robot operation tasks. Users input their work details and operation history by voice, the data is analyzed in real time on a cloud server, and the results are displayed on smart glasses. A specific embodiment is shown below.

[0633] System program and processing description

[0634] Voice input

[0635] The user wears smart glasses and inputs operation details and maintenance activities via voice. The speech_recognition library is used for voice input. When the user speaks into the glasses, the voice is converted into text data.

[0636] Data transmission and real-time analysis

[0637] The text data converted from the speech is automatically sent to a cloud server. The cloud server uses natural language processing (NLP) technology to analyze the input data in real time. The analysis includes information such as the equipment used, problems encountered, and operational errors.

[0638] Report generation and display

[0639] The cloud server automatically generates a report based on the analysis results. The report includes an evaluation of the operation and suggestions for improvement. This report is displayed on the smart glasses' screen, allowing the user to view the analysis results in real time.

[0640] Hardware and software used

[0641] Hardware: Smart glasses, cloud server, microphone

[0642] Software: speech_recognition library, Natural Language Processing (NLP) module

[0643] Specific example

[0644] Examples of use by factory employees

[0645] For example, if a factory worker says, "Replace the belt on robot A on line 2 and check for oil leaks," the system records that information. The recorded data is sent to a cloud server in real time, and feedback such as "Belt replacement is correct. Oil leak update needed" is displayed on the smart glasses' screen.

[0646] Example of a prompt

[0647] Examples of prompts used by factory workers to give more specific instructions on what to do are as follows:

[0648] Please analyze the following work procedure: "Replace the belt on robot A in line 2 and check for oil leaks."

[0649] Please output an evaluation of the work performed and suggestions for improvement as part of the analysis results.

[0650] This system improves the efficiency of factory operations and enables rapid problem solving, significantly reducing the effort required for manual data entry and task organization.

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] The user wears smart glasses and inputs the operation details by voice. The user speaks, "Replace the belt on robot A on line 2 and check for oil leaks." This voice input is converted into text data by the smart glasses' microphone and the speech_recognition library.

[0654] Input: Audio data

[0655] Output: Text data (Example: "Robot A on Line 2: Belt replacement, oil leak check")

[0656] Step 2:

[0657] The device (smart glasses) sends the converted text data to the cloud server. The text data is sent to the cloud server using a secure communication protocol (e.g., HTTPS).

[0658] Input: Text data

[0659] Output: Notification of completion of data transmission to cloud server

[0660] Step 3:

[0661] The server passes the received text data to a natural language processing (NLP) module for real-time analysis. Specifically, the data is processed to extract information on the usage status, problems, and operational errors of various equipment within the text data. Generative AI models are also utilized in the analysis.

[0662] Input: Text data

[0663] Output: Analysis results data (e.g., belt replacement appropriate, oil leak location needs updating)

[0664] Step 4:

[0665] The server automatically generates a report based on the analysis results. The report includes an evaluation of user actions and suggestions for improvement. The report is converted to a standard format (e.g., JSON, PDF).

[0666] Input: Analysis result data

[0667] Output: Generated report (e.g., evaluation and suggestion data in JSON format)

[0668] Step 5:

[0669] The server generates a report and sends it to the device (smart glasses). A secure communication protocol is used to transmit data from the cloud server to the smart glasses.

[0670] Input: Generated report

[0671] Output: Notification that the report has been successfully sent to the smart glasses.

[0672] Step 6:

[0673] The device (smart glasses) displays the received report on the user interface. The user can view the analysis results and improvement suggestions in real time through the smart glasses. Specifically, the smart glasses' display visually highlights the key points of the report.

[0674] Input: Generated report

[0675] Output: Report content displayed on the smart glasses screen (e.g., belt replacement appropriate, oil leak location needs updating)

[0676] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0677] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized by utilizing an emotion engine in addition to data exchange between the user, terminal, and server.

[0678] System Configuration

[0679] 1. The user enters and uploads the details of their work:

[0680] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[0681] The terminal receives input data and files from the user and checks their format and content.

[0682] 2. The server analyzes the data:

[0683] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[0684] The server's natural language processing (NLP) module analyzes the text data and extracts data points such as the type of work, project size, achievement level, and skills used.

[0685] 3. Emotion recognition using an emotion engine:

[0686] During the data analysis process, the server uses an emotion engine to analyze emotional information from user input or uploaded data.

[0687] The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[0688] 4. The server automatically generates reports:

[0689] Based on the analysis results and the emotion evaluation by the emotion engine, the server organizes the user's work history and generates a report.

[0690] The report includes categories such as "Overall Performance," "Skill Analysis," "Sentiment Assessment," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate annually, areas of expertise, and areas for future improvement, as well as the evolution of emotional states.

[0691] The server saves reports generated by the server for each user and allows them to be output in PDF or HTML format as needed.

[0692] 5. The device presents the report to the user:

[0693] The server sends the URL or file of the generated report to the terminal.

[0694] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill assessments, sentiment assessments, and areas for improvement.

[0695] Specific example

[0696] Example 1: User A, a sales professional

[0697] 1. Login and data entry / upload:

[0698] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[0699] 2. Data analysis and sentiment recognition:

[0700] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them. The emotion engine analyzes emotional information from user A's input data and evaluates emotions such as "satisfaction" and "frustration."

[0701] 3. Report generation:

[0702] The server generates reports based on user A's work performance, categorized into "annual sales achievement rate," "customer satisfaction," "sales area," and "emotional evaluation."

[0703] 4. Report submission:

[0704] The device displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement, as well as emotional trends, and to plan future actions.

[0705] Example 2: In the case of user B, an engineer.

[0706] 1. Login and data entry / upload:

[0707] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0708] 2. Data analysis and sentiment recognition:

[0709] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate. The emotion engine analyzes emotional information from user B's input data and evaluates emotions such as "excitement" and "stress."

[0710] 3. Report generation:

[0711] The server generates a report that includes User B's skills matrix, case studies of successful projects, and suggestions for future skill development. The report also includes an emotional assessment, visualizing the ongoing changes in User B's emotional state during work.

[0712] 4. Report submission:

[0713] The device displays the generated report, and by reviewing it, User B can gain a clear understanding of their strengths, the skills they need to learn in the future, and even emotional trends.

[0714] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback and emotional evaluation, thereby contributing to the development of career plans and the improvement of skills.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[0718] Step 2:

[0719] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[0720] Step 3:

[0721] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[0722] Step 4:

[0723] The server uses an emotion engine to analyze emotional information from user input or uploaded data in parallel with the analysis. The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited). This emotional evaluation is also used as input data when the server generates automated reports.

[0724] Step 5:

[0725] The server organizes the user's work history based on analysis results and sentiment evaluations, and automatically generates a report. The report includes categories such as "Overall Work Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate per year, areas of expertise, and areas for future improvement, as well as the evolution of the user's emotional state. The server saves the generated report for each user and makes it possible to output it in PDF or HTML format as needed.

[0726] Step 6:

[0727] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill assessment, sentiment assessment, and areas for improvement. For example, specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved" is visualized, as well as sentiment assessments such as "Stress levels increased in recent projects."

[0728] This allows users to understand not only their work content and results, but also their emotional trends, which can be used to build career plans and improve their skills.

[0729] (Example 2)

[0730] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0731] Conventional business process inventory systems analyzed business data and generated reports without considering the user's emotional state. As a result, there was a lack of feedback and improvement suggestions based on user emotions, making it difficult to improve user satisfaction and engagement. Furthermore, insufficient data formatting checks and the use of emotion recognition technology made it difficult to generate accurate and useful reports.

[0732] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input or upload work content, means for the terminal to check the format and content of the data, means for the terminal to transmit the checked data to the server, means for the server to analyze the received data, means for the server to analyze emotional information using emotional recognition technology, means for the server to generate an automatic report based on the analysis results and emotional evaluation, and means for the terminal to present the report to the user. This makes it possible to provide feedback and improvement suggestions based on emotional information in addition to the user's work content.

[0733] A "user" refers to a person who inputs or uploads data such as work details and project history into the system.

[0734] A "terminal" refers to a device or software that receives input data and files from a user, checks their format and content, and sends the data to a server.

[0735] A "server" refers to a computer system that analyzes received data, performs emotional analysis, and automatically generates reports based on the analysis results.

[0736] "Data format" refers to the file format of the data entered or uploaded by the user, such as CSV or Excel files.

[0737] "Data content" refers to specific information about work details and project history included in the data entered or uploaded by the user.

[0738] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords and numerical information.

[0739] "Emotion recognition technology" refers to technology that analyzes emotional tone from text data and evaluates the user's emotional state.

[0740] An "automated report" refers to a report generated based on analysis results and sentiment evaluations, and includes categories such as work performance, skill analysis, sentiment evaluation, and improvement suggestions.

[0741] An "input form" refers to an online form used by users to input details about their work or project history.

[0742] An "analysis module" refers to a software component used by a server to analyze the data it receives.

[0743] "Encryption" refers to the technology of encoding information in order to transmit data securely.

[0744] "Emotional tone" refers to the emotional nuances, such as positive, negative, or neutral, contained within text data.

[0745] "Feedback" refers to timely and appropriate evaluations and advice provided to users based on analysis results and sentiment assessments.

[0746] This invention relates to a system that supports the inventory of work content, recognizes user emotions, and provides feedback based on the analysis results. This system is realized by utilizing an emotion engine while the user, terminal, and server exchange data.

[0747] Main System Configuration

[0748] 1. The user enters and uploads the details of their work:

[0749] Users enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files). The terminal receives the input data or files from the user and checks their format and content.

[0750] 2. The device checks the data:

[0751] The terminal checks the format and content of the data received from the user. If the data format is incorrect or required fields are missing, an error message is displayed to the user.

[0752] 3. The device sends data to the server:

[0753] The terminal sends the checked data to the server. The data is encrypted during transmission, ensuring secure communication.

[0754] Hardware and software used

[0755] Hardware: Devices (computers, smartphones, etc.), servers

[0756] software:

[0757] Web form and file upload functionality

[0758] Natural Language Processing (NLP) modules (e.g., SpaCy, NLTK)

[0759] Sentiment recognition engines (e.g., IBM Watson, Microsoft Azure Text Analytics)

[0760] Report generation modules (e.g., JasperReports, Apache FOP)

[0761] HTTPS protocol for encrypted and secure communication

[0762] Specific example

[0763] Example 1: User A, a sales professional

[0764] 1. The user inputs and uploads their work details: User A logs into the system and uploads a CSV file containing sales performance data for the past year.

[0765] 2. The terminal checks the data: The terminal checks the format of the CSV data and verifies that all necessary items are included.

[0766] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[0767] Example 2: In the case of user B, an engineer.

[0768] 1. The user inputs and uploads their work details: User B logs into the system and uploads a CSV file containing their development project history and skill set.

[0769] 2. The terminal checks the data: The terminal checks the format and content of the CSV data and verifies that there are no missing items.

[0770] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[0771] Example of a prompt

[0772] "I have entered and uploaded sales performance data for the past year. Please analyze this data and generate a report evaluating sales achievement rate, customer satisfaction, and sentiment tone."

[0773] In this way, we aim to contribute to the development of users' career plans and skill improvement by comprehensively managing users' work content and emotional evaluations, and providing more accurate feedback and improvement suggestions.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] Users input or upload details of their work.

[0777] Specific actions:

[0778] Users log in to the system and enter their work details and project history into an input form. Alternatively, they can upload existing data files (e.g., Excel or CSV files).

[0779] input:

[0780] Text data entered by the user or a CSV file uploaded by the user.

[0781] output:

[0782] The input data file transferred to the terminal.

[0783] Step 2:

[0784] The device checks the data format and content.

[0785] Specific actions:

[0786] The terminal checks the file format of the entered or uploaded data (whether it's CSV, Excel, etc.) and verifies that the content itself contains all required fields (e.g., project name, start date, end date). If there are any deficiencies in the format or content, an error message is displayed to the user.

[0787] input:

[0788] Data files received from the user.

[0789] output:

[0790] If the data format and content are correct, it will be marked as checked data. If there are errors in the format or content, an error message will be displayed.

[0791] Step 3:

[0792] The terminal sends the checked data to the server.

[0793] Specific actions:

[0794] The terminal encrypts the checked data and sends it to the server using a secure communication protocol (HTTPS). This ensures the security of the data.

[0795] input:

[0796] Checked data.

[0797] output:

[0798] The encrypted data is sent to the server.

[0799] Step 4:

[0800] The server analyzes the data it receives.

[0801] Specific actions:

[0802] The server receives the data and passes it to an analysis module (e.g., SpaCy, NLTK). The NLP module analyzes the data and extracts data points such as the type of work, project size, achievement level, and skills used.

[0803] input:

[0804] Encrypted data.

[0805] output:

[0806] Analyzed data points (e.g., type of work, project size, achievement level, skills used).

[0807] Step 5:

[0808] The server analyzes emotional information using emotion recognition technology.

[0809] Specific actions:

[0810] The server passes data acquired from the analysis module to an emotion recognition engine (e.g., IBM Watson, Microsoft Azure Text Analytics). The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[0811] input:

[0812] Analyzed data points.

[0813] output:

[0814] Results of the assessment of emotional tone and emotional state.

[0815] Step 6:

[0816] The server automatically generates a report based on the analysis results and sentiment evaluation.

[0817] Specific actions:

[0818] The server launches a report generation module (e.g., JasperReports) and creates a report based on the analysis results and sentiment evaluation, including categories such as "Overall Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." The report is output in PDF or HTML format.

[0819] input:

[0820] Results of the assessment of emotional tone and emotional state.

[0821] output:

[0822] An automatically generated report (in PDF or HTML format).

[0823] Step 7:

[0824] The server sends the URL or file of the generated report to the terminal.

[0825] Specific actions:

[0826] The server re-encrypts the generated report file and sends it to the terminal via URL or file. HTTPS is used as the communication protocol.

[0827] input:

[0828] An automatically generated report.

[0829] output:

[0830] An encrypted report file is sent to the terminal.

[0831] Step 8:

[0832] The device presents the report to the user.

[0833] Specific actions:

[0834] The terminal decrypts the received report file and displays it on the user interface. Users can then review their work performance, skill evaluations, sentiment evaluations, and improvement suggestions.

[0835] input:

[0836] Encrypted report file.

[0837] output:

[0838] A report displayed in the user interface.

[0839] (Application Example 2)

[0840] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0841] Conventional business process inventory systems failed to take user emotions into account during analysis, making it impossible to understand users' emotional burdens and stress levels. Furthermore, in factory robot maintenance, there was a lack of feedback reflecting the emotional state of technicians, posing challenges to improving maintenance efficiency and technician satisfaction. This invention aims to solve these problems.

[0842] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0843] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to analyze emotional information using an emotion engine, means for generating an automatic report that includes maintenance history and the emotional evaluation of the technician, and means for the terminal to present the report to the user. This enables detailed feedback including emotional evaluation in addition to the user's work details, and in particular, enables the creation of efficient maintenance plans that reflect the emotional state of the technician in the maintenance of factory robots.

[0844] A "user" is an individual or group that uses the system to input work details or upload data.

[0845] "Job description" refers to the detailed information about the daily tasks and projects that users perform.

[0846] "Input" refers to the act of a user manually providing data to a system.

[0847] "Uploading" refers to the act of a user importing a pre-prepared file into the system.

[0848] "Means" refers to the methods or devices used to achieve a specific objective.

[0849] A "server" is a computer system that stores, processes, and analyzes data.

[0850] "Analysis" is the process of breaking down data into smaller parts and understanding and evaluating its contents.

[0851] An "emotion engine" is software or a system used to analyze the emotions behind text data obtained from users.

[0852] "Emotional information" refers to data that indicates the user's emotional state based on text data analyzed by the emotion engine.

[0853] An "automated report" is a report generated by the server based on analysis results and sentiment information.

[0854] "Maintenance history" refers to detailed records of maintenance work and repairs performed on factory robots.

[0855] A "technician" is a specialist who performs maintenance on factory robots.

[0856] "Presentation" refers to the act of showing or providing a generated report to the user.

[0857] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized through data exchange between the user, terminal, and server, as well as the use of an emotion engine.

[0858] System Configuration

[0859] 1. The user enters and uploads the details of their work.

[0860] Users enter their work details and maintenance history into an input form, or upload existing data files (e.g., Excel or CSV files). Users perform these operations after logging into a specific system.

[0861] The terminal receives input data and files from the user and checks their format and content.

[0862] 2. The server analyzes the data.

[0863] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[0864] The server's natural language processing module (e.g., pandas, TextBlob) analyzes the maintenance history and extracts data points such as the type and frequency of work and the number of times parts were replaced. The server then uses an emotion engine to analyze sentiment information from comments written by users.

[0865] 3. Emotion recognition using an emotion engine

[0866] The server analyzes the emotional tone (e.g., positive, negative, neutral) from the text data of maintenance records and evaluates the emotional state of the technicians. The results of the emotion engine's analysis are stored in a database.

[0867] 4. The server automatically generates reports.

[0868] Based on the analysis results and the emotion evaluation by the emotion engine, the server automatically generates a report that includes maintenance history, frequency of parts replacement, and the emotion evaluation of the technicians. The report records in detail the maintenance status of each robot, the emotional state of the technicians, and the degree of wear and tear on the parts.

[0869] The server saves the generated report in PDF or HTML format and sends it to the terminal as needed.

[0870] 5. The device presents the report to the user.

[0871] The terminal displays the URL or file of the report received from the server in the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. Based on this information, future maintenance plans and training programs for technicians are developed.

[0872] Specific example

[0873] For example, if a technician comments, "There are too many parts replacements, and it's stressful," the emotion engine will rate this comment as a negative emotion. The server will then generate an automated report that includes this emotion rating and present the results to the technician and administrator to provide specific improvement suggestions and feedback.

[0874] Example of a prompt

[0875] Examples of prompt statements to input into a generative AI model include the following:

[0876] "Analyze the maintenance history of factory robots and evaluate the sentiment of the technicians. The data will include comments such as:

[0877] 'Too many parts need replacing, it's stressful.'

[0878] 'I was satisfied because the work went smoothly.'

[0879] 'I'm having trouble using the new tool.'

[0880] Based on these comments, please evaluate them as positive, negative, or neutral and reflect this in your report.

[0881] Based on this configuration, the system of the present invention efficiently organizes the user's work content and emotional state, and provides accurate feedback and emotional evaluation, enabling the creation of efficient maintenance plans that take into account the emotional information of technicians, particularly in the maintenance of factory robots.

[0882] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0883] Step 1:

[0884] Users input and upload details of their work.

[0885] Users log in to the system and enter their work details and maintenance history into input forms, or upload existing data files (e.g., Excel or CSV files). Based on the input, the terminal checks the data format and content and saves it in the appropriate format. It verifies whether the input data format is correct and displays an error message if it is in an inappropriate format.

[0886] Step 2:

[0887] The device sends data to the server.

[0888] The terminal sends data entered or uploaded by the user to the server. Before sending data, it checks the data format and converts it to a format that the server can parse. The input data sent by the terminal to the server includes work content data and maintenance history data, while the output is formatted data.

[0889] Step 3:

[0890] The server analyzes the data.

[0891] The server passes the received data to an analysis module. Using a natural language processing module, the maintenance history is analyzed, and data points such as the type and frequency of work and the number of times parts were replaced are extracted. The input is data provided by the user, and the output is the analyzed data points. The server then performs a detailed analysis based on these points.

[0892] Step 4:

[0893] The server uses an emotion engine to analyze emotional information.

[0894] The server uses an emotion engine to analyze emotional information from user input and uploaded data. Specifically, it evaluates the emotional tone (positive, negative, neutral) of text data and analyzes the emotional state of the engineers. The input is text data including user comments, and the output is the analyzed emotional information.

[0895] Step 5:

[0896] The server automatically generates reports.

[0897] The server automatically generates a report based on the analysis results and the sentiment evaluation by the sentiment engine. The report includes maintenance history, frequency of parts replacement, and the technician's sentiment evaluation. The input is the analysis results up to the previous step, and the output is a document formatted as a report. This ensures that the maintenance status of the robot, the sentiment state of the technician, and the degree of wear and tear on the parts are recorded in detail.

[0898] Step 6:

[0899] The device presents the report to the user.

[0900] The terminal displays the URL or file of the report received from the server on the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. The input is the URL or file of the generated report, and the output is displayed on the user interface. Based on this information, users can develop future maintenance plans and training programs for technicians.

[0901] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0902] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0903] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0904] [Third Embodiment]

[0905] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0906] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0907] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0908] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0909] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0910] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0911] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0912] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0913] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0914] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0915] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0916] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0917] This invention is a system that supports the inventory of work content. It allows users to organize their own work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server.

[0918] System Configuration

[0919] 1. The user enters and uploads the details of their work:

[0920] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[0921] The terminal receives input data and files from the user and first checks their format and content.

[0922] 2. The server analyzes the data:

[0923] The device sends the data it receives to the server.

[0924] The server passes the received data to an analysis module, which then uses natural language processing technology to analyze the business content data.

[0925] This analysis allows the server to extract data points such as the type of work, project size, achievement level, and skills used.

[0926] 3. The server automatically generates reports:

[0927] Based on the analysis results, the server organizes the user's work history and creates reports categorized into, for example, "overall work performance," "skill analysis," and "improvement suggestions."

[0928] The reports will be generated in PDF or HTML format, making them easy for users to review.

[0929] 4. The device presents the report to the user:

[0930] The server sends the generated report to the terminal.

[0931] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill evaluations, and areas for improvement.

[0932] Specific example

[0933] Example 1: User A, a sales professional

[0934] 1. Login and data entry / upload:

[0935] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[0936] 2. Data Analysis:

[0937] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them.

[0938] 3. Report generation:

[0939] The server generates reports based on user A's work performance, categorized as "annual sales achievement rate," "customer satisfaction," and "sales area."

[0940] 4. Report submission:

[0941] The terminal displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement and develop a plan of action for the future.

[0942] Example 2: In the case of user B, an engineer.

[0943] 1. Login and data entry / upload:

[0944] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[0945] 2. Data Analysis:

[0946] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate.

[0947] 3. Report generation:

[0948] The server generates a report that includes User B's skill matrix, case studies of successful projects, and suggestions for future skill development.

[0949] 4. Report submission:

[0950] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[0951] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback, thereby contributing to the development of career plans and the improvement of skills.

[0952] The following describes the processing flow.

[0953] Step 1:

[0954] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[0955] Step 2:

[0956] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[0957] Step 3:

[0958] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[0959] Step 4:

[0960] The server analyzes the results and generates a report summarizing the user's work history. The report includes categories such as "Overall Work Performance," "Skill Analysis," and "Improvement Suggestions." For example, it details the number of projects completed annually, success rate, areas of expertise, and skill areas that need further development. The server saves the generated report for each user and allows output in PDF or HTML format as needed.

[0961] Step 5:

[0962] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill evaluation, and areas for improvement. For example, they may see specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved."

[0963] (Example 1)

[0964] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0965] Traditional work inventory systems struggled to efficiently analyze user-entered data and provide detailed feedback. Furthermore, insufficient checks on data format and content could lead to erroneous analysis results. Additionally, the cumbersome report generation and display process made it difficult for users to quickly grasp their own work performance and skill assessments.

[0966] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0967] In this invention, the server includes means for the user to input or upload work details, means for the terminal to check the format and content of the data received from the user, means for the terminal to send the verified data to the server, means for the server to analyze the received data, means for the server to create reports for each category based on the analysis results, means for the server to generate the reports in PDF or HTML format, and means for the terminal to display the generated reports on the user interface. This makes it possible to efficiently analyze the user's work details and provide accurate feedback. In addition, since the format and content of the data are checked, the reliability of the analysis results is improved, and the user can quickly grasp their work performance and skill evaluation.

[0968] A "user" is an individual or legal entity that uses the system and inputs or uploads information about their work or project history.

[0969] A "terminal" is a device used by users to input or upload work details and project history, and it is a device that checks the format and content of the data and communicates data with the server.

[0970] A "server" is a computer system that analyzes data received from terminals and generates reports based on the results.

[0971] An "input form" is an online data entry interface that allows users to manually enter details of their work and project history.

[0972] "Uploading" refers to the act of a user transferring data files, including work details and project history, to a system via their device.

[0973] "Checking data format and content" is a verification process in which the terminal checks the integrity and format of the data received from the user and sends it to the server in a state suitable for analysis.

[0974] "Analysis" is the process of analyzing work content, skills, and project history based on data received by the server, and extracting important data points.

[0975] "Natural language processing technology" is a computer technology that allows servers to understand and analyze the meaning of text data obtained from users.

[0976] A "category" is a broad classification used to categorize analysis results, and examples include overall business performance, skill analysis, and improvement suggestions.

[0977] A "report" is a document generated by the server based on the analysis results, and it serves as a report for users to review their work performance and skill evaluations.

[0978] "PDF format" is an abbreviation for Portable Document Format, and it is one of the fixed-layout document file formats.

[0979] "HTML format" is an abbreviation for HyperText Markup Language, and it is one of the markup languages ​​used to describe web pages.

[0980] A "user interface" refers to the screens and operational areas on a device that allow the user and system to interact, and is the interface where generated reports are displayed.

[0981] Modes for carrying out the invention

[0982] This invention relates to a system that supports the inventory of work content. The system allows users to organize their work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server. The following describes an embodiment of this system in detail.

[0983] Program Overview

[0984] This system involves a series of processes in which users input or upload work details and project history, a server analyzes that data, and automatically generates and presents reports to the user. Each step is implemented using specific hardware and software.

[0985] 1. User input and upload of work details

[0986] Users log in to the system using their terminals and input their work details and project history, or upload existing data files (e.g., Excel or CSV). This allows users to easily provide data to the system. The input form is an online data entry interface. [Specific example: User A fills in their past sales performance in the input form and uploads an Excel file.]

[0987] 2. Checking the data format and content using the terminal.

[0988] The terminal checks the format and content of the received data and sends the validated data to the server. The "pandas" library is used for data validation. This ensures that data suitable for analysis is delivered to the server. [Specific example: The terminal validates the data format and content of an Excel file to verify its consistency.]

[0989] 3. Data analysis by the server

[0990] The server analyzes the received data and extracts important data points using natural language processing techniques. Natural language processing libraries such as "spaCy" and "NLTK" are used. Machine learning libraries such as "scikit-learn" and "TensorFlow" are also used to extract data points. [Specific example: The server analyzes important data such as sales achievement rates and project completion rates.]

[0991] 4. Automatic report generation by the server

[0992] The server generates reports based on the analysis results and outputs them in PDF or HTML format. "Jinja2" and "ReportLab" are used for report generation, making it easy for users to review the results. [Specific example: The server creates reports categorized by "Annual Sales Achievement Rate," "Customer Satisfaction," etc., and outputs them as PDFs.]

[0993] 5. Presentation of the report

[0994] The terminal displays the generated report in the user interface. Through this, the user can review their work performance, skill evaluation, and areas for improvement. The interface is provided using the front-end frameworks "React" and "Vue.js". [Specific example: The terminal displays a PDF report, and user A views it to check their performance.]

[0995] Specific example

[0996] Example 1: User A, a sales professional

[0997] 1. Login and data entry / upload:

[0998] User A logs into the system and fills in past sales performance and project management results in an input form or uploads an Excel file.

[0999] 2. Check the format and content of the data:

[1000] The terminal checks the data it receives and verifies its format and content.

[1001] 3. Data Analysis:

[1002] The server analyzes the verified data and extracts data points such as sales achievement rates and customer feedback.

[1003] 4. Report generation:

[1004] Based on the analysis results, the server creates reports categorized by "annual sales achievement rate," "customer satisfaction," etc., and generates them in PDF format.

[1005] 5. Report submission:

[1006] The terminal displays the generated report on the user interface, and user A reviews it.

[1007] Example 2: In the case of user B, an engineer.

[1008] 1. Login and data entry / upload:

[1009] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[1010] 2. Check the format and content of the data:

[1011] The terminal checks the format and content of the data it receives and sends the verified data to the server.

[1012] 3. Data Analysis:

[1013] The server analyzes the data and extracts data points such as the development language used, tools, and project success rate.

[1014] 4. Report generation:

[1015] The server generates a report based on the analysis results, which includes a skills matrix, case studies of successful projects, and suggestions for future skill development.

[1016] 5. Report submission:

[1017] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[1018] This system efficiently organizes users' work content and provides accurate feedback, thereby contributing to career planning and skill improvement.

[1019] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1020] Step 1:

[1021] Users enter or upload details of their work.

[1022] Users either enter their work details and project history into an input form on the terminal, or upload existing data files (e.g., Excel or CSV files). Specifically, the user logs into the system and enters the necessary information on the input screen, or drags and drops existing performance data. The entered data or uploaded file is imported into the terminal as input data.

[1023] Input: User-entered work details, project history, or uploaded data files.

[1024] Output: Data imported into the terminal

[1025] Step 2:

[1026] The device checks the format and content of the data it receives from the user.

[1027] The terminal checks the format and content of the received data, verifying its integrity and format. Specifically, it uses the "pandas" library to validate the data. This validation process checks, for example, whether all necessary fields are filled in and whether the data format is correct.

[1028] Input: Data imported from the user

[1029] Output: Data with verified format and content.

[1030] Step 3:

[1031] The device sends verified data to the server.

[1032] The device sends verified data to the server via an HTTP request. This process uses libraries such as "axios" or "requests" to send and receive data. Specifically, the device requests data from the server, and the server receives it.

[1033] Input: Data with validated format and content.

[1034] Output: Data received by the server

[1035] Step 4:

[1036] The server analyzes the data.

[1037] The server passes the received data to an analysis module, which then analyzes the data using natural language processing techniques. Libraries such as "spaCy" and "NLTK" are used for this analysis. The server extracts data points such as the type of work, project size, and achievement level, and performs analysis using libraries such as "scikit-learn" and "TensorFlow".

[1038] Input: Data sent to the server

[1039] Output: Extracted data points

[1040] Step 5:

[1041] The server generates reports categorized by type based on the analysis results.

[1042] Based on the analysis results, the server organizes the data by category (e.g., "Overall Business Performance," "Skill Analysis," "Improvement Suggestions") and creates a report. Template engines and document generation libraries such as "Jinja2" and "ReportLab" are used to generate the report. Specifically, the server organizes the analysis results and groups them into categories.

[1043] Input: Extracted data points

[1044] Output: Reports organized by category

[1045] Step 6:

[1046] The server generates reports in PDF or HTML format.

[1047] The server generates the created report in PDF or HTML format. Libraries such as "ReportLab" are used for PDF generation, and "Jinja2" for HTML generation. Specifically, the server applies the report data to a template and generates a report file in the appropriate format.

[1048] Input: Reports organized by category

[1049] Output: Reports in PDF or HTML format

[1050] Step 7:

[1051] The server sends the generated report to the terminal.

[1052] The server sends the generated report to the terminal. This uses the HTTP protocol and the "axios" or "requests" library. Specifically, the server sends a request to the terminal to send the report file, and the terminal receives it.

[1053] Input: PDF or HTML report

[1054] Output: Report received by the terminal

[1055] Step 8:

[1056] The terminal displays the generated report in the user interface.

[1057] The device displays the report received from the server in the user interface. Frontend frameworks such as "React" or "Vue.js" are used for this. Specifically, the device opens a report display screen and presents it to the user in an easy-to-understand manner.

[1058] Input: Report received by the device

[1059] Output: Report displayed in the user interface

[1060] (Application Example 1)

[1061] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1062] Traditional systems for organizing and analyzing business processes required users to manually input large amounts of data, which often resulted in missing or incorrect information. Furthermore, it was difficult to receive immediate feedback on analysis results, hindering direct improvements in work efficiency and skill development. This limitation was particularly evident in situations requiring real-time business improvement, such as factory operations.

[1063] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1064] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to automatically generate a report based on the analysis results, means for the terminal to present the report to the user, means for the user to input operation details by voice, means for sending data to a cloud server for real-time analysis, and means for displaying the analysis results on a user interface. This reduces the burden on the user to manually input data and allows them to receive analysis results as real-time feedback. This enables improved work efficiency and rapid problem solving in field operations.

[1065] "Means for users to input or upload work details" refers to methods for users to directly input information about their work or to upload existing data files to the system.

[1066] "Means of analyzing data received by the server" refers to the process by which the server performs analysis based on business data received from the user.

[1067] "A means by which a server automatically generates reports based on analysis results" refers to a method in which a server uses the analyzed data to automatically create reports containing useful information for the user.

[1068] "Means by which a terminal presents a report to a user" refers to a method of displaying the generated report on the user's terminal so that the user can review its contents.

[1069] "A means for users to input their operations by voice" refers to a method for users to record their operations by voice and input them into the system.

[1070] "A method for sending data to a cloud server and analyzing it in real time" refers to a method of sending voice or input data from the user to a cloud server and performing immediate analysis.

[1071] "Means of displaying analysis results on a user interface" refers to a method of displaying the analyzed results on a user interface so that the user can intuitively understand the results.

[1072] This invention is a system that supports the efficiency improvement and suggestions for improvements in factory robot operation tasks. Users input their work details and operation history by voice, the data is analyzed in real time on a cloud server, and the results are displayed on smart glasses. A specific embodiment is shown below.

[1073] System program and processing description

[1074] Voice input

[1075] The user wears smart glasses and inputs operation details and maintenance activities via voice. The speech_recognition library is used for voice input. When the user speaks into the glasses, the voice is converted into text data.

[1076] Data transmission and real-time analysis

[1077] The text data converted from the speech is automatically sent to a cloud server. The cloud server uses natural language processing (NLP) technology to analyze the input data in real time. The analysis includes information such as the equipment used, problems encountered, and operational errors.

[1078] Report generation and display

[1079] The cloud server automatically generates a report based on the analysis results. The report includes an evaluation of the operation and suggestions for improvement. This report is displayed on the smart glasses' screen, allowing the user to view the analysis results in real time.

[1080] Hardware and software used

[1081] Hardware: Smart glasses, cloud server, microphone

[1082] Software: speech_recognition library, Natural Language Processing (NLP) module

[1083] Specific example

[1084] Examples of use by factory employees

[1085] For example, if a factory worker says, "Replace the belt on robot A on line 2 and check for oil leaks," the system records that information. The recorded data is sent to a cloud server in real time, and feedback such as "Belt replacement is correct. Oil leak update needed" is displayed on the smart glasses' screen.

[1086] Example of a prompt

[1087] Examples of prompts used by factory workers to give more specific instructions on what to do are as follows:

[1088] Please analyze the following work procedure: "Replace the belt on robot A in line 2 and check for oil leaks."

[1089] Please output an evaluation of the work performed and suggestions for improvement as part of the analysis results.

[1090] This system improves the efficiency of factory operations and enables rapid problem solving, significantly reducing the effort required for manual data entry and task organization.

[1091] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1092] Step 1:

[1093] The user wears smart glasses and inputs the operation details by voice. The user speaks, "Replace the belt on robot A on line 2 and check for oil leaks." This voice input is converted into text data by the smart glasses' microphone and the speech_recognition library.

[1094] Input: Audio data

[1095] Output: Text data (Example: "Robot A on Line 2: Belt replacement, oil leak check")

[1096] Step 2:

[1097] The device (smart glasses) sends the converted text data to the cloud server. The text data is sent to the cloud server using a secure communication protocol (e.g., HTTPS).

[1098] Input: Text data

[1099] Output: Notification of completion of data transmission to cloud server

[1100] Step 3:

[1101] The server passes the received text data to a natural language processing (NLP) module for real-time analysis. Specifically, the data is processed to extract information on the usage status, problems, and operational errors of various equipment within the text data. Generative AI models are also utilized in the analysis.

[1102] Input: Text data

[1103] Output: Analysis results data (e.g., belt replacement appropriate, oil leak location needs updating)

[1104] Step 4:

[1105] The server automatically generates a report based on the analysis results. The report includes an evaluation of user actions and suggestions for improvement. The report is converted to a standard format (e.g., JSON, PDF).

[1106] Input: Analysis result data

[1107] Output: Generated report (e.g., evaluation and suggestion data in JSON format)

[1108] Step 5:

[1109] The server generates a report and sends it to the device (smart glasses). A secure communication protocol is used to transmit data from the cloud server to the smart glasses.

[1110] Input: Generated report

[1111] Output: Notification that the report has been successfully sent to the smart glasses.

[1112] Step 6:

[1113] The device (smart glasses) displays the received report on the user interface. The user can view the analysis results and improvement suggestions in real time through the smart glasses. Specifically, the smart glasses' display visually highlights the key points of the report.

[1114] Input: Generated report

[1115] Output: Report content displayed on the smart glasses screen (e.g., belt replacement appropriate, oil leak location needs updating)

[1116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1117] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized by utilizing an emotion engine in addition to data exchange between the user, terminal, and server.

[1118] System Configuration

[1119] 1. The user enters and uploads the details of their work:

[1120] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[1121] The terminal receives input data and files from the user and checks their format and content.

[1122] 2. The server analyzes the data:

[1123] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[1124] The server's natural language processing (NLP) module analyzes the text data and extracts data points such as the type of work, project size, achievement level, and skills used.

[1125] 3. Emotion recognition using an emotion engine:

[1126] During the data analysis process, the server uses an emotion engine to analyze emotional information from user input or uploaded data.

[1127] The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[1128] 4. The server automatically generates reports:

[1129] Based on the analysis results and the emotion evaluation by the emotion engine, the server organizes the user's work history and generates a report.

[1130] The report includes categories such as "Overall Performance," "Skill Analysis," "Sentiment Assessment," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate annually, areas of expertise, and areas for future improvement, as well as the evolution of emotional states.

[1131] The server saves reports generated by the server for each user and allows them to be output in PDF or HTML format as needed.

[1132] 5. The device presents the report to the user:

[1133] The server sends the URL or file of the generated report to the terminal.

[1134] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill assessments, sentiment assessments, and areas for improvement.

[1135] Specific example

[1136] Example 1: User A, a sales professional

[1137] 1. Login and data entry / upload:

[1138] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[1139] 2. Data analysis and sentiment recognition:

[1140] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them. The emotion engine analyzes emotional information from user A's input data and evaluates emotions such as "satisfaction" and "frustration."

[1141] 3. Report generation:

[1142] The server generates reports based on user A's work performance, categorized into "annual sales achievement rate," "customer satisfaction," "sales area," and "emotional evaluation."

[1143] 4. Report submission:

[1144] The device displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement, as well as emotional trends, and to plan future actions.

[1145] Example 2: In the case of user B, an engineer.

[1146] 1. Login and data entry / upload:

[1147] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[1148] 2. Data analysis and sentiment recognition:

[1149] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate. The emotion engine analyzes emotional information from user B's input data and evaluates emotions such as "excitement" and "stress."

[1150] 3. Report generation:

[1151] The server generates a report that includes User B's skills matrix, case studies of successful projects, and suggestions for future skill development. The report also includes an emotional assessment, visualizing the ongoing changes in User B's emotional state during work.

[1152] 4. Report submission:

[1153] The device displays the generated report, and by reviewing it, User B can gain a clear understanding of their strengths, the skills they need to learn in the future, and even emotional trends.

[1154] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback and emotional evaluation, thereby contributing to the development of career plans and the improvement of skills.

[1155] The following describes the processing flow.

[1156] Step 1:

[1157] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[1158] Step 2:

[1159] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[1160] Step 3:

[1161] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[1162] Step 4:

[1163] The server uses an emotion engine to analyze emotional information from user input or uploaded data in parallel with the analysis. The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited). This emotional evaluation is also used as input data when the server generates automated reports.

[1164] Step 5:

[1165] The server organizes the user's work history based on analysis results and sentiment evaluations, and automatically generates a report. The report includes categories such as "Overall Work Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate per year, areas of expertise, and areas for future improvement, as well as the evolution of the user's emotional state. The server saves the generated report for each user and makes it possible to output it in PDF or HTML format as needed.

[1166] Step 6:

[1167] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill assessment, sentiment assessment, and areas for improvement. For example, specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved" is visualized, as well as sentiment assessments such as "Stress levels increased in recent projects."

[1168] This allows users to understand not only their work content and results, but also their emotional trends, which can be used to build career plans and improve their skills.

[1169] (Example 2)

[1170] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1171] Conventional business process inventory systems analyzed business data and generated reports without considering the user's emotional state. As a result, there was a lack of feedback and improvement suggestions based on user emotions, making it difficult to improve user satisfaction and engagement. Furthermore, insufficient data formatting checks and the use of emotion recognition technology made it difficult to generate accurate and useful reports.

[1172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input or upload work content, means for the terminal to check the format and content of the data, means for the terminal to transmit the checked data to the server, means for the server to analyze the received data, means for the server to analyze emotional information using emotional recognition technology, means for the server to generate an automatic report based on the analysis results and emotional evaluation, and means for the terminal to present the report to the user. This makes it possible to provide feedback and improvement suggestions based on emotional information in addition to the user's work content.

[1173] A "user" refers to a person who inputs or uploads data such as work details and project history into the system.

[1174] A "terminal" refers to a device or software that receives input data and files from a user, checks their format and content, and sends the data to a server.

[1175] A "server" refers to a computer system that analyzes received data, performs emotional analysis, and automatically generates reports based on the analysis results.

[1176] "Data format" refers to the file format of the data entered or uploaded by the user, such as CSV or Excel files.

[1177] "Data content" refers to specific information about work details and project history included in the data entered or uploaded by the user.

[1178] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords and numerical information.

[1179] "Emotion recognition technology" refers to technology that analyzes emotional tone from text data and evaluates the user's emotional state.

[1180] An "automated report" refers to a report generated based on analysis results and sentiment evaluations, and includes categories such as work performance, skill analysis, sentiment evaluation, and improvement suggestions.

[1181] An "input form" refers to an online form used by users to input details about their work or project history.

[1182] An "analysis module" refers to a software component used by a server to analyze the data it receives.

[1183] "Encryption" refers to the technology of encoding information in order to transmit data securely.

[1184] "Emotional tone" refers to the emotional nuances, such as positive, negative, or neutral, contained within text data.

[1185] "Feedback" refers to timely and appropriate evaluations and advice provided to users based on analysis results and sentiment assessments.

[1186] This invention relates to a system that supports the inventory of work content, recognizes user emotions, and provides feedback based on the analysis results. This system is realized by utilizing an emotion engine while the user, terminal, and server exchange data.

[1187] Main System Configuration

[1188] 1. The user enters and uploads the details of their work:

[1189] Users enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files). The terminal receives the input data or files from the user and checks their format and content.

[1190] 2. The device checks the data:

[1191] The terminal checks the format and content of the data received from the user. If the data format is incorrect or required fields are missing, an error message is displayed to the user.

[1192] 3. The device sends data to the server:

[1193] The terminal sends the checked data to the server. The data is encrypted during transmission, ensuring secure communication.

[1194] Hardware and software used

[1195] Hardware: Devices (computers, smartphones, etc.), servers

[1196] software:

[1197] Web form and file upload functionality

[1198] Natural Language Processing (NLP) modules (e.g., SpaCy, NLTK)

[1199] Sentiment recognition engines (e.g., IBM Watson, Microsoft Azure Text Analytics)

[1200] Report generation modules (e.g., JasperReports, Apache FOP)

[1201] HTTPS protocol for encrypted and secure communication

[1202] Specific example

[1203] Example 1: User A, a sales professional

[1204] 1. The user inputs and uploads their work details: User A logs into the system and uploads a CSV file containing sales performance data for the past year.

[1205] 2. The terminal checks the data: The terminal checks the format of the CSV data and verifies that all necessary items are included.

[1206] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[1207] Example 2: In the case of user B, an engineer.

[1208] 1. The user inputs and uploads their work details: User B logs into the system and uploads a CSV file containing their development project history and skill set.

[1209] 2. The terminal checks the data: The terminal checks the format and content of the CSV data and verifies that there are no missing items.

[1210] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[1211] Example of a prompt

[1212] "I have entered and uploaded sales performance data for the past year. Please analyze this data and generate a report evaluating sales achievement rate, customer satisfaction, and sentiment tone."

[1213] In this way, we aim to contribute to the development of users' career plans and skill improvement by comprehensively managing users' work content and emotional evaluations, and providing more accurate feedback and improvement suggestions.

[1214] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1215] Step 1:

[1216] Users input or upload details of their work.

[1217] Specific actions:

[1218] Users log in to the system and enter their work details and project history into an input form. Alternatively, they can upload existing data files (e.g., Excel or CSV files).

[1219] input:

[1220] Text data entered by the user or a CSV file uploaded by the user.

[1221] output:

[1222] The input data file transferred to the terminal.

[1223] Step 2:

[1224] The device checks the data format and content.

[1225] Specific actions:

[1226] The terminal checks the file format of the entered or uploaded data (whether it's CSV, Excel, etc.) and verifies that the content itself contains all required fields (e.g., project name, start date, end date). If there are any deficiencies in the format or content, an error message is displayed to the user.

[1227] input:

[1228] Data files received from the user.

[1229] output:

[1230] If the data format and content are correct, it will be marked as checked data. If there are errors in the format or content, an error message will be displayed.

[1231] Step 3:

[1232] The terminal sends the checked data to the server.

[1233] Specific actions:

[1234] The terminal encrypts the checked data and sends it to the server using a secure communication protocol (HTTPS). This ensures the security of the data.

[1235] input:

[1236] Checked data.

[1237] output:

[1238] The encrypted data is sent to the server.

[1239] Step 4:

[1240] The server analyzes the data it receives.

[1241] Specific actions:

[1242] The server receives the data and passes it to an analysis module (e.g., SpaCy, NLTK). The NLP module analyzes the data and extracts data points such as the type of work, project size, achievement level, and skills used.

[1243] input:

[1244] Encrypted data.

[1245] output:

[1246] Analyzed data points (e.g., type of work, project size, achievement level, skills used).

[1247] Step 5:

[1248] The server analyzes emotional information using emotion recognition technology.

[1249] Specific actions:

[1250] The server passes data acquired from the analysis module to an emotion recognition engine (e.g., IBM Watson, Microsoft Azure Text Analytics). The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[1251] input:

[1252] Analyzed data points.

[1253] output:

[1254] Results of the assessment of emotional tone and emotional state.

[1255] Step 6:

[1256] The server automatically generates a report based on the analysis results and sentiment evaluation.

[1257] Specific actions:

[1258] The server launches a report generation module (e.g., JasperReports) and creates a report based on the analysis results and sentiment evaluation, including categories such as "Overall Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." The report is output in PDF or HTML format.

[1259] input:

[1260] Results of the assessment of emotional tone and emotional state.

[1261] output:

[1262] An automatically generated report (in PDF or HTML format).

[1263] Step 7:

[1264] The server sends the URL or file of the generated report to the terminal.

[1265] Specific actions:

[1266] The server re-encrypts the generated report file and sends it to the terminal via URL or file. HTTPS is used as the communication protocol.

[1267] input:

[1268] An automatically generated report.

[1269] output:

[1270] An encrypted report file is sent to the terminal.

[1271] Step 8:

[1272] The device presents the report to the user.

[1273] Specific actions:

[1274] The terminal decrypts the received report file and displays it on the user interface. Users can then review their work performance, skill evaluations, sentiment evaluations, and improvement suggestions.

[1275] input:

[1276] Encrypted report file.

[1277] output:

[1278] A report displayed in the user interface.

[1279] (Application Example 2)

[1280] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1281] Conventional business process inventory systems failed to take user emotions into account during analysis, making it impossible to understand users' emotional burdens and stress levels. Furthermore, in factory robot maintenance, there was a lack of feedback reflecting the emotional state of technicians, posing challenges to improving maintenance efficiency and technician satisfaction. This invention aims to solve these problems.

[1282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1283] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to analyze emotional information using an emotion engine, means for generating an automatic report that includes maintenance history and the emotional evaluation of the technician, and means for the terminal to present the report to the user. This enables detailed feedback including emotional evaluation in addition to the user's work details, and in particular, enables the creation of efficient maintenance plans that reflect the emotional state of the technician in the maintenance of factory robots.

[1284] A "user" is an individual or group that uses the system to input work details or upload data.

[1285] "Job description" refers to the detailed information about the daily tasks and projects that users perform.

[1286] "Input" refers to the act of a user manually providing data to a system.

[1287] "Uploading" refers to the act of a user importing a pre-prepared file into the system.

[1288] "Means" refers to the methods or devices used to achieve a specific objective.

[1289] A "server" is a computer system that stores, processes, and analyzes data.

[1290] "Analysis" is the process of breaking down data into smaller parts and understanding and evaluating its contents.

[1291] An "emotion engine" is software or a system used to analyze the emotions behind text data obtained from users.

[1292] "Emotional information" refers to data that indicates the user's emotional state based on text data analyzed by the emotion engine.

[1293] An "automated report" is a report generated by the server based on analysis results and sentiment information.

[1294] "Maintenance history" refers to detailed records of maintenance work and repairs performed on factory robots.

[1295] A "technician" is a specialist who performs maintenance on factory robots.

[1296] "Presentation" refers to the act of showing or providing a generated report to the user.

[1297] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized through data exchange between the user, terminal, and server, as well as the use of an emotion engine.

[1298] System Configuration

[1299] 1. The user enters and uploads the details of their work.

[1300] Users enter their work details and maintenance history into an input form, or upload existing data files (e.g., Excel or CSV files). Users perform these operations after logging into a specific system.

[1301] The terminal receives input data and files from the user and checks their format and content.

[1302] 2. The server analyzes the data.

[1303] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[1304] The server's natural language processing module (e.g., pandas, TextBlob) analyzes the maintenance history and extracts data points such as the type and frequency of work and the number of times parts were replaced. The server then uses an emotion engine to analyze sentiment information from comments written by users.

[1305] 3. Emotion recognition using an emotion engine

[1306] The server analyzes the emotional tone (e.g., positive, negative, neutral) from the text data of maintenance records and evaluates the emotional state of the technicians. The results of the emotion engine's analysis are stored in a database.

[1307] 4. The server automatically generates reports.

[1308] Based on the analysis results and the emotion evaluation by the emotion engine, the server automatically generates a report that includes maintenance history, frequency of parts replacement, and the emotion evaluation of the technicians. The report records in detail the maintenance status of each robot, the emotional state of the technicians, and the degree of wear and tear on the parts.

[1309] The server saves the generated report in PDF or HTML format and sends it to the terminal as needed.

[1310] 5. The device presents the report to the user.

[1311] The terminal displays the URL or file of the report received from the server in the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. Based on this information, future maintenance plans and training programs for technicians are developed.

[1312] Specific example

[1313] For example, if a technician comments, "There are too many parts replacements, and it's stressful," the emotion engine will rate this comment as a negative emotion. The server will then generate an automated report that includes this emotion rating and present the results to the technician and administrator to provide specific improvement suggestions and feedback.

[1314] Example of a prompt

[1315] Examples of prompt statements to input into a generative AI model include the following:

[1316] "Analyze the maintenance history of factory robots and evaluate the sentiment of the technicians. The data will include comments such as:

[1317] 'Too many parts need replacing, it's stressful.'

[1318] 'I was satisfied because the work went smoothly.'

[1319] 'I'm having trouble using the new tool.'

[1320] Based on these comments, please evaluate them as positive, negative, or neutral and reflect this in your report.

[1321] Based on this configuration, the system of the present invention efficiently organizes the user's work content and emotional state, and provides accurate feedback and emotional evaluation, enabling the creation of efficient maintenance plans that take into account the emotional information of technicians, particularly in the maintenance of factory robots.

[1322] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1323] Step 1:

[1324] Users input and upload details of their work.

[1325] Users log in to the system and enter their work details and maintenance history into input forms, or upload existing data files (e.g., Excel or CSV files). Based on the input, the terminal checks the data format and content and saves it in the appropriate format. It verifies whether the input data format is correct and displays an error message if it is in an inappropriate format.

[1326] Step 2:

[1327] The device sends data to the server.

[1328] The terminal sends data entered or uploaded by the user to the server. Before sending data, it checks the data format and converts it to a format that the server can parse. The input data sent by the terminal to the server includes work content data and maintenance history data, while the output is formatted data.

[1329] Step 3:

[1330] The server analyzes the data.

[1331] The server passes the received data to an analysis module. Using a natural language processing module, the maintenance history is analyzed, and data points such as the type and frequency of work and the number of times parts were replaced are extracted. The input is data provided by the user, and the output is the analyzed data points. The server then performs a detailed analysis based on these points.

[1332] Step 4:

[1333] The server uses an emotion engine to analyze emotional information.

[1334] The server uses an emotion engine to analyze emotional information from user input and uploaded data. Specifically, it evaluates the emotional tone (positive, negative, neutral) of text data and analyzes the emotional state of the engineers. The input is text data including user comments, and the output is the analyzed emotional information.

[1335] Step 5:

[1336] The server automatically generates reports.

[1337] The server automatically generates a report based on the analysis results and the sentiment evaluation by the sentiment engine. The report includes maintenance history, frequency of parts replacement, and the technician's sentiment evaluation. The input is the analysis results up to the previous step, and the output is a document formatted as a report. This ensures that the maintenance status of the robot, the sentiment state of the technician, and the degree of wear and tear on the parts are recorded in detail.

[1338] Step 6:

[1339] The device presents the report to the user.

[1340] The terminal displays the URL or file of the report received from the server on the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. The input is the URL or file of the generated report, and the output is displayed on the user interface. Based on this information, users can develop future maintenance plans and training programs for technicians.

[1341] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1342] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1343] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1344] [Fourth Embodiment]

[1345] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1346] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1347] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1348] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1349] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1350] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1351] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1352] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1353] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1354] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1355] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1356] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1357] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1358] This invention is a system that supports the inventory of work content. It allows users to organize their own work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server.

[1359] System Configuration

[1360] 1. The user enters and uploads the details of their work:

[1361] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[1362] The terminal receives input data and files from the user and first checks their format and content.

[1363] 2. The server analyzes the data:

[1364] The device sends the data it receives to the server.

[1365] The server passes the received data to an analysis module, which then uses natural language processing technology to analyze the business content data.

[1366] This analysis allows the server to extract data points such as the type of work, project size, achievement level, and skills used.

[1367] 3. The server automatically generates reports:

[1368] Based on the analysis results, the server organizes the user's work history and creates reports categorized into, for example, "overall work performance," "skill analysis," and "improvement suggestions."

[1369] The reports will be generated in PDF or HTML format, making them easy for users to review.

[1370] 4. The device presents the report to the user:

[1371] The server sends the generated report to the terminal.

[1372] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill evaluations, and areas for improvement.

[1373] Specific example

[1374] Example 1: User A, a sales professional

[1375] 1. Login and data entry / upload:

[1376] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[1377] 2. Data Analysis:

[1378] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them.

[1379] 3. Report generation:

[1380] The server generates reports based on user A's work performance, categorized as "annual sales achievement rate," "customer satisfaction," and "sales area."

[1381] 4. Report submission:

[1382] The terminal displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement and develop a plan of action for the future.

[1383] Example 2: In the case of user B, an engineer.

[1384] 1. Login and data entry / upload:

[1385] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[1386] 2. Data Analysis:

[1387] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate.

[1388] 3. Report generation:

[1389] The server generates a report that includes User B's skill matrix, case studies of successful projects, and suggestions for future skill development.

[1390] 4. Report submission:

[1391] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[1392] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback, thereby contributing to the development of career plans and the improvement of skills.

[1393] The following describes the processing flow.

[1394] Step 1:

[1395] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[1396] Step 2:

[1397] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[1398] Step 3:

[1399] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[1400] Step 4:

[1401] The server analyzes the results and generates a report summarizing the user's work history. The report includes categories such as "Overall Work Performance," "Skill Analysis," and "Improvement Suggestions." For example, it details the number of projects completed annually, success rate, areas of expertise, and skill areas that need further development. The server saves the generated report for each user and allows output in PDF or HTML format as needed.

[1402] Step 5:

[1403] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill evaluation, and areas for improvement. For example, they may see specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved."

[1404] (Example 1)

[1405] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1406] Traditional work inventory systems struggled to efficiently analyze user-entered data and provide detailed feedback. Furthermore, insufficient checks on data format and content could lead to erroneous analysis results. Additionally, the cumbersome report generation and display process made it difficult for users to quickly grasp their own work performance and skill assessments.

[1407] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1408] In this invention, the server includes means for the user to input or upload work details, means for the terminal to check the format and content of the data received from the user, means for the terminal to send the verified data to the server, means for the server to analyze the received data, means for the server to create reports for each category based on the analysis results, means for the server to generate the reports in PDF or HTML format, and means for the terminal to display the generated reports on the user interface. This makes it possible to efficiently analyze the user's work details and provide accurate feedback. In addition, since the format and content of the data are checked, the reliability of the analysis results is improved, and the user can quickly grasp their work performance and skill evaluation.

[1409] A "user" is an individual or legal entity that uses the system and inputs or uploads information about their work or project history.

[1410] A "terminal" is a device used by users to input or upload work details and project history, and it is a device that checks the format and content of the data and communicates data with the server.

[1411] A "server" is a computer system that analyzes data received from terminals and generates reports based on the results.

[1412] An "input form" is an online data entry interface that allows users to manually enter details of their work and project history.

[1413] "Uploading" refers to the act of a user transferring data files, including work details and project history, to a system via their device.

[1414] "Checking data format and content" is a verification process in which the terminal checks the integrity and format of the data received from the user and sends it to the server in a state suitable for analysis.

[1415] "Analysis" is the process of analyzing work content, skills, and project history based on data received by the server, and extracting important data points.

[1416] "Natural language processing technology" is a computer technology that allows servers to understand and analyze the meaning of text data obtained from users.

[1417] A "category" is a broad classification used to categorize analysis results, and examples include overall business performance, skill analysis, and improvement suggestions.

[1418] A "report" is a document generated by the server based on the analysis results, and it serves as a report for users to review their work performance and skill evaluations.

[1419] "PDF format" is an abbreviation for Portable Document Format, and it is one of the fixed-layout document file formats.

[1420] "HTML format" is an abbreviation for HyperText Markup Language, and it is one of the markup languages ​​used to describe web pages.

[1421] A "user interface" refers to the screens and operational areas on a device that allow the user and system to interact, and is the interface where generated reports are displayed.

[1422] Modes for carrying out the invention

[1423] This invention relates to a system that supports the inventory of work content. The system allows users to organize their work content and project history, analyze it on a server, and provide feedback based on the results. This system is realized through data exchange between the user, terminal, and server. The following describes an embodiment of this system in detail.

[1424] Program Overview

[1425] This system involves a series of processes in which users input or upload work details and project history, a server analyzes that data, and automatically generates and presents reports to the user. Each step is implemented using specific hardware and software.

[1426] 1. User input and upload of work details

[1427] Users log in to the system using their terminals and input their work details and project history, or upload existing data files (e.g., Excel or CSV). This allows users to easily provide data to the system. The input form is an online data entry interface. [Specific example: User A fills in their past sales performance in the input form and uploads an Excel file.]

[1428] 2. Checking the data format and content using the terminal.

[1429] The terminal checks the format and content of the received data and sends the validated data to the server. The "pandas" library is used for data validation. This ensures that data suitable for analysis is delivered to the server. [Specific example: The terminal validates the data format and content of an Excel file to verify its consistency.]

[1430] 3. Data analysis by the server

[1431] The server analyzes the received data and extracts important data points using natural language processing techniques. Natural language processing libraries such as "spaCy" and "NLTK" are used. Machine learning libraries such as "scikit-learn" and "TensorFlow" are also used to extract data points. [Specific example: The server analyzes important data such as sales achievement rates and project completion rates.]

[1432] 4. Automatic report generation by the server

[1433] The server generates reports based on the analysis results and outputs them in PDF or HTML format. "Jinja2" and "ReportLab" are used for report generation, making it easy for users to review the results. [Specific example: The server creates reports categorized by "Annual Sales Achievement Rate," "Customer Satisfaction," etc., and outputs them as PDFs.]

[1434] 5. Presentation of the report

[1435] The terminal displays the generated report in the user interface. Through this, the user can review their work performance, skill evaluation, and areas for improvement. The interface is provided using the front-end frameworks "React" and "Vue.js". [Specific example: The terminal displays a PDF report, and user A views it to check their performance.]

[1436] Specific example

[1437] Example 1: User A, a sales professional

[1438] 1. Login and data entry / upload:

[1439] User A logs into the system and fills in past sales performance and project management results in an input form or uploads an Excel file.

[1440] 2. Check the format and content of the data:

[1441] The terminal checks the data it receives and verifies its format and content.

[1442] 3. Data Analysis:

[1443] The server analyzes the verified data and extracts data points such as sales achievement rates and customer feedback.

[1444] 4. Report generation:

[1445] Based on the analysis results, the server creates reports categorized by "annual sales achievement rate," "customer satisfaction," etc., and generates them in PDF format.

[1446] 5. Report submission:

[1447] The terminal displays the generated report on the user interface, and user A reviews it.

[1448] Example 2: In the case of user B, an engineer.

[1449] 1. Login and data entry / upload:

[1450] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[1451] 2. Check the format and content of the data:

[1452] The terminal checks the format and content of the data it receives and sends the verified data to the server.

[1453] 3. Data Analysis:

[1454] The server analyzes the data and extracts data points such as the development language used, tools, and project success rate.

[1455] 4. Report generation:

[1456] The server generates a report based on the analysis results, which includes a skills matrix, case studies of successful projects, and suggestions for future skill development.

[1457] 5. Report submission:

[1458] The device displays the generated report, and by user B reviewing it, their strengths and the technologies they need to learn in the future become clear.

[1459] This system efficiently organizes users' work content and provides accurate feedback, thereby contributing to career planning and skill improvement.

[1460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1461] Step 1:

[1462] Users enter or upload details of their work.

[1463] Users either enter their work details and project history into an input form on the terminal, or upload existing data files (e.g., Excel or CSV files). Specifically, the user logs into the system and enters the necessary information on the input screen, or drags and drops existing performance data. The entered data or uploaded file is imported into the terminal as input data.

[1464] Input: User-entered work details, project history, or uploaded data files.

[1465] Output: Data imported into the terminal

[1466] Step 2:

[1467] The device checks the format and content of the data it receives from the user.

[1468] The terminal checks the format and content of the received data, verifying its integrity and format. Specifically, it uses the "pandas" library to validate the data. This validation process checks, for example, whether all necessary fields are filled in and whether the data format is correct.

[1469] Input: Data imported from the user

[1470] Output: Data with verified format and content.

[1471] Step 3:

[1472] The device sends verified data to the server.

[1473] The device sends verified data to the server via an HTTP request. This process uses libraries such as "axios" or "requests" to send and receive data. Specifically, the device requests data from the server, and the server receives it.

[1474] Input: Data with validated format and content.

[1475] Output: Data received by the server

[1476] Step 4:

[1477] The server analyzes the data.

[1478] The server passes the received data to an analysis module, which then analyzes the data using natural language processing techniques. Libraries such as "spaCy" and "NLTK" are used for this analysis. The server extracts data points such as the type of work, project size, and achievement level, and performs analysis using libraries such as "scikit-learn" and "TensorFlow".

[1479] Input: Data sent to the server

[1480] Output: Extracted data points

[1481] Step 5:

[1482] The server generates reports categorized by type based on the analysis results.

[1483] Based on the analysis results, the server organizes the data by category (e.g., "Overall Business Performance," "Skill Analysis," "Improvement Suggestions") and creates a report. Template engines and document generation libraries such as "Jinja2" and "ReportLab" are used to generate the report. Specifically, the server organizes the analysis results and groups them into categories.

[1484] Input: Extracted data points

[1485] Output: Reports organized by category

[1486] Step 6:

[1487] The server generates reports in PDF or HTML format.

[1488] The server generates the created report in PDF or HTML format. Libraries such as "ReportLab" are used for PDF generation, and "Jinja2" for HTML generation. Specifically, the server applies the report data to a template and generates a report file in the appropriate format.

[1489] Input: Reports organized by category

[1490] Output: Reports in PDF or HTML format

[1491] Step 7:

[1492] The server sends the generated report to the terminal.

[1493] The server sends the generated report to the terminal. This uses the HTTP protocol and the "axios" or "requests" library. Specifically, the server sends a request to the terminal to send the report file, and the terminal receives it.

[1494] Input: PDF or HTML report

[1495] Output: Report received by the terminal

[1496] Step 8:

[1497] The terminal displays the generated report in the user interface.

[1498] The device displays the report received from the server in the user interface. Frontend frameworks such as "React" or "Vue.js" are used for this. Specifically, the device opens a report display screen and presents it to the user in an easy-to-understand manner.

[1499] Input: Report received by the device

[1500] Output: Report displayed in the user interface

[1501] (Application Example 1)

[1502] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1503] Traditional systems for organizing and analyzing business processes required users to manually input large amounts of data, which often resulted in missing or incorrect information. Furthermore, it was difficult to receive immediate feedback on analysis results, hindering direct improvements in work efficiency and skill development. This limitation was particularly evident in situations requiring real-time business improvement, such as factory operations.

[1504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1505] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to automatically generate a report based on the analysis results, means for the terminal to present the report to the user, means for the user to input operation details by voice, means for sending data to a cloud server for real-time analysis, and means for displaying the analysis results on a user interface. This reduces the burden on the user to manually input data and allows them to receive analysis results as real-time feedback. This enables improved work efficiency and rapid problem solving in field operations.

[1506] "Means for users to input or upload work details" refers to methods for users to directly input information about their work or to upload existing data files to the system.

[1507] "Means of analyzing data received by the server" refers to the process by which the server performs analysis based on business data received from the user.

[1508] "A means by which a server automatically generates reports based on analysis results" refers to a method in which a server uses the analyzed data to automatically create reports containing useful information for the user.

[1509] "Means by which a terminal presents a report to a user" refers to a method of displaying the generated report on the user's terminal so that the user can review its contents.

[1510] "A means for users to input their operations by voice" refers to a method for users to record their operations by voice and input them into the system.

[1511] "A method for sending data to a cloud server and analyzing it in real time" refers to a method of sending voice or input data from the user to a cloud server and performing immediate analysis.

[1512] "Means of displaying analysis results on a user interface" refers to a method of displaying the analyzed results on a user interface so that the user can intuitively understand the results.

[1513] This invention is a system that supports the efficiency improvement and suggestions for improvements in factory robot operation tasks. Users input their work details and operation history by voice, the data is analyzed in real time on a cloud server, and the results are displayed on smart glasses. A specific embodiment is shown below.

[1514] System program and processing description

[1515] Voice input

[1516] The user wears smart glasses and inputs operation details and maintenance activities via voice. The speech_recognition library is used for voice input. When the user speaks into the glasses, the voice is converted into text data.

[1517] Data transmission and real-time analysis

[1518] The text data converted from the speech is automatically sent to a cloud server. The cloud server uses natural language processing (NLP) technology to analyze the input data in real time. The analysis includes information such as the equipment used, problems encountered, and operational errors.

[1519] Report generation and display

[1520] The cloud server automatically generates a report based on the analysis results. The report includes an evaluation of the operation and suggestions for improvement. This report is displayed on the smart glasses' screen, allowing the user to view the analysis results in real time.

[1521] Hardware and software used

[1522] Hardware: Smart glasses, cloud server, microphone

[1523] Software: speech_recognition library, Natural Language Processing (NLP) module

[1524] Specific example

[1525] Examples of use by factory employees

[1526] For example, if a factory worker says, "Replace the belt on robot A on line 2 and check for oil leaks," the system records that information. The recorded data is sent to a cloud server in real time, and feedback such as "Belt replacement is correct. Oil leak update needed" is displayed on the smart glasses' screen.

[1527] Example of a prompt

[1528] Examples of prompts used by factory workers to give more specific instructions on what to do are as follows:

[1529] Please analyze the following work procedure: "Replace the belt on robot A in line 2 and check for oil leaks."

[1530] Please output an evaluation of the work performed and suggestions for improvement as part of the analysis results.

[1531] This system improves the efficiency of factory operations and enables rapid problem solving, significantly reducing the effort required for manual data entry and task organization.

[1532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1533] Step 1:

[1534] The user wears smart glasses and inputs the operation details by voice. The user speaks, "Replace the belt on robot A on line 2 and check for oil leaks." This voice input is converted into text data by the smart glasses' microphone and the speech_recognition library.

[1535] Input: Audio data

[1536] Output: Text data (Example: "Robot A on Line 2: Belt replacement, oil leak check")

[1537] Step 2:

[1538] The device (smart glasses) sends the converted text data to the cloud server. The text data is sent to the cloud server using a secure communication protocol (e.g., HTTPS).

[1539] Input: Text data

[1540] Output: Notification of completion of data transmission to cloud server

[1541] Step 3:

[1542] The server passes the received text data to a natural language processing (NLP) module for real-time analysis. Specifically, the data is processed to extract information on the usage status, problems, and operational errors of various equipment within the text data. Generative AI models are also utilized in the analysis.

[1543] Input: Text data

[1544] Output: Analysis results data (e.g., belt replacement appropriate, oil leak location needs updating)

[1545] Step 4:

[1546] The server automatically generates a report based on the analysis results. The report includes an evaluation of user actions and suggestions for improvement. The report is converted to a standard format (e.g., JSON, PDF).

[1547] Input: Analysis result data

[1548] Output: Generated report (e.g., evaluation and suggestion data in JSON format)

[1549] Step 5:

[1550] The server generates a report and sends it to the device (smart glasses). A secure communication protocol is used to transmit data from the cloud server to the smart glasses.

[1551] Input: Generated report

[1552] Output: Notification that the report has been successfully sent to the smart glasses.

[1553] Step 6:

[1554] The device (smart glasses) displays the received report on the user interface. The user can view the analysis results and improvement suggestions in real time through the smart glasses. Specifically, the smart glasses' display visually highlights the key points of the report.

[1555] Input: Generated report

[1556] Output: Report content displayed on the smart glasses screen (e.g., belt replacement appropriate, oil leak location needs updating)

[1557] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1558] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized by utilizing an emotion engine in addition to data exchange between the user, terminal, and server.

[1559] System Configuration

[1560] 1. The user enters and uploads the details of their work:

[1561] Users can enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files).

[1562] The terminal receives input data and files from the user and checks their format and content.

[1563] 2. The server analyzes the data:

[1564] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[1565] The server's natural language processing (NLP) module analyzes the text data and extracts data points such as the type of work, project size, achievement level, and skills used.

[1566] 3. Emotion recognition using an emotion engine:

[1567] During the data analysis process, the server uses an emotion engine to analyze emotional information from user input or uploaded data.

[1568] The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[1569] 4. The server automatically generates reports:

[1570] Based on the analysis results and the emotion evaluation by the emotion engine, the server organizes the user's work history and generates a report.

[1571] The report includes categories such as "Overall Performance," "Skill Analysis," "Sentiment Assessment," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate annually, areas of expertise, and areas for future improvement, as well as the evolution of emotional states.

[1572] The server saves reports generated by the server for each user and allows them to be output in PDF or HTML format as needed.

[1573] 5. The device presents the report to the user:

[1574] The server sends the URL or file of the generated report to the terminal.

[1575] The terminal receives reports and displays them in the user interface, allowing users to recognize their own work performance, skill assessments, sentiment assessments, and areas for improvement.

[1576] Specific example

[1577] Example 1: User A, a sales professional

[1578] 1. Login and data entry / upload:

[1579] User A logs into the system and fills in their past sales performance and project management results in an input form, or uploads an Excel file.

[1580] 2. Data analysis and sentiment recognition:

[1581] The server receives the data, extracts keywords related to sales achievement rates, customer feedback, and project management skills, and analyzes them. The emotion engine analyzes emotional information from user A's input data and evaluates emotions such as "satisfaction" and "frustration."

[1582] 3. Report generation:

[1583] The server generates reports based on user A's work performance, categorized into "annual sales achievement rate," "customer satisfaction," "sales area," and "emotional evaluation."

[1584] 4. Report submission:

[1585] The device displays the generated report, which User A reviews. This allows User A to understand their strengths and areas for improvement, as well as emotional trends, and to plan future actions.

[1586] Example 2: In the case of user B, an engineer.

[1587] 1. Login and data entry / upload:

[1588] User B logs into the system, enters their development project history and skill set, and uploads a CSV file of their project report.

[1589] 2. Data analysis and sentiment recognition:

[1590] The server analyzes the received data and extracts data points such as the development language used, tools, and project success rate. The emotion engine analyzes emotional information from user B's input data and evaluates emotions such as "excitement" and "stress."

[1591] 3. Report generation:

[1592] The server generates a report that includes User B's skills matrix, case studies of successful projects, and suggestions for future skill development. The report also includes an emotional assessment, visualizing the ongoing changes in User B's emotional state during work.

[1593] 4. Report submission:

[1594] The device displays the generated report, and by reviewing it, User B can gain a clear understanding of their strengths, the skills they need to learn in the future, and even emotional trends.

[1595] Based on these specific procedures, the system of the present invention efficiently organizes the user's work content and provides accurate feedback and emotional evaluation, thereby contributing to the development of career plans and the improvement of skills.

[1596] The following describes the processing flow.

[1597] Step 1:

[1598] The user accesses the system and opens the login page. The user enters their ID and password and clicks the login button. The terminal sends the entered information to the server. The server compares the received ID and password with the authentication database, and if correct, sends an authentication success message to the terminal. If authentication fails, an error message is returned.

[1599] Step 2:

[1600] The user opens the work details input page. The user enters detailed information about their work performance and project history into the form, or selects an existing work data file (e.g., Excel or CSV) and clicks the upload button. The terminal receives the entered data or uploaded file, checks its format and content, and then sends it to the server. If there are any errors in the format or content, the terminal displays an error message and prompts the user to make corrections.

[1601] Step 3:

[1602] The server passes the received data to an analysis module. The server's natural language processing (NLP) module analyzes the text data and extracts important keywords and phrases such as "project management," "sales achievement," and "customer satisfaction." The server classifies the work content by type and calculates data points such as results, skill sets, and achievement levels for each project.

[1603] Step 4:

[1604] The server uses an emotion engine to analyze emotional information from user input or uploaded data in parallel with the analysis. The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited). This emotional evaluation is also used as input data when the server generates automated reports.

[1605] Step 5:

[1606] The server organizes the user's work history based on analysis results and sentiment evaluations, and automatically generates a report. The report includes categories such as "Overall Work Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." For example, it details the number of projects completed and their success rate per year, areas of expertise, and areas for future improvement, as well as the evolution of the user's emotional state. The server saves the generated report for each user and makes it possible to output it in PDF or HTML format as needed.

[1607] Step 6:

[1608] The server sends the URL or file of the generated report to the terminal. The terminal receives the report URL or file and makes it available for the user to view. The user reviews the report and becomes aware of their work performance, skill assessment, sentiment assessment, and areas for improvement. For example, specific feedback such as "Negotiation skills are highly rated" or "Time management should be improved" is visualized, as well as sentiment assessments such as "Stress levels increased in recent projects."

[1609] This allows users to understand not only their work content and results, but also their emotional trends, which can be used to build career plans and improve their skills.

[1610] (Example 2)

[1611] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1612] Conventional business process inventory systems analyzed business data and generated reports without considering the user's emotional state. As a result, there was a lack of feedback and improvement suggestions based on user emotions, making it difficult to improve user satisfaction and engagement. Furthermore, insufficient data formatting checks and the use of emotion recognition technology made it difficult to generate accurate and useful reports.

[1613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input or upload work content, means for the terminal to check the format and content of the data, means for the terminal to transmit the checked data to the server, means for the server to analyze the received data, means for the server to analyze emotional information using emotional recognition technology, means for the server to generate an automatic report based on the analysis results and emotional evaluation, and means for the terminal to present the report to the user. This makes it possible to provide feedback and improvement suggestions based on emotional information in addition to the user's work content.

[1614] A "user" refers to a person who inputs or uploads data such as work details and project history into the system.

[1615] A "terminal" refers to a device or software that receives input data and files from a user, checks their format and content, and sends the data to a server.

[1616] A "server" refers to a computer system that analyzes received data, performs emotional analysis, and automatically generates reports based on the analysis results.

[1617] "Data format" refers to the file format of the data entered or uploaded by the user, such as CSV or Excel files.

[1618] "Data content" refers to specific information about work details and project history included in the data entered or uploaded by the user.

[1619] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords and numerical information.

[1620] "Emotion recognition technology" refers to technology that analyzes emotional tone from text data and evaluates the user's emotional state.

[1621] An "automated report" refers to a report generated based on analysis results and sentiment evaluations, and includes categories such as work performance, skill analysis, sentiment evaluation, and improvement suggestions.

[1622] An "input form" refers to an online form used by users to input details about their work or project history.

[1623] An "analysis module" refers to a software component used by a server to analyze the data it receives.

[1624] "Encryption" refers to the technology of encoding information in order to transmit data securely.

[1625] "Emotional tone" refers to the emotional nuances, such as positive, negative, or neutral, contained within text data.

[1626] "Feedback" refers to timely and appropriate evaluations and advice provided to users based on analysis results and sentiment assessments.

[1627] This invention relates to a system that supports the inventory of work content, recognizes user emotions, and provides feedback based on the analysis results. This system is realized by utilizing an emotion engine while the user, terminal, and server exchange data.

[1628] Main System Configuration

[1629] 1. The user enters and uploads the details of their work:

[1630] Users enter their work details and project history into an input form, or upload existing data files (e.g., Excel or CSV files). The terminal receives the input data or files from the user and checks their format and content.

[1631] 2. The device checks the data:

[1632] The terminal checks the format and content of the data received from the user. If the data format is incorrect or required fields are missing, an error message is displayed to the user.

[1633] 3. The device sends data to the server:

[1634] The terminal sends the checked data to the server. The data is encrypted during transmission, ensuring secure communication.

[1635] Hardware and software used

[1636] Hardware: Devices (computers, smartphones, etc.), servers

[1637] software:

[1638] Web form and file upload functionality

[1639] Natural Language Processing (NLP) modules (e.g., SpaCy, NLTK)

[1640] Sentiment recognition engines (e.g., IBM Watson, Microsoft Azure Text Analytics)

[1641] Report generation modules (e.g., JasperReports, Apache FOP)

[1642] HTTPS protocol for encrypted and secure communication

[1643] Specific example

[1644] Example 1: User A, a sales professional

[1645] 1. The user inputs and uploads their work details: User A logs into the system and uploads a CSV file containing sales performance data for the past year.

[1646] 2. The terminal checks the data: The terminal checks the format of the CSV data and verifies that all necessary items are included.

[1647] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[1648] Example 2: In the case of user B, an engineer.

[1649] 1. The user inputs and uploads their work details: User B logs into the system and uploads a CSV file containing their development project history and skill set.

[1650] 2. The terminal checks the data: The terminal checks the format and content of the CSV data and verifies that there are no missing items.

[1651] 3. The device sends data to the server: The device encrypts the checked data and sends it to the server.

[1652] Example of a prompt

[1653] "I have entered and uploaded sales performance data for the past year. Please analyze this data and generate a report evaluating sales achievement rate, customer satisfaction, and sentiment tone."

[1654] In this way, we aim to contribute to the development of users' career plans and skill improvement by comprehensively managing users' work content and emotional evaluations, and providing more accurate feedback and improvement suggestions.

[1655] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1656] Step 1:

[1657] Users input or upload details of their work.

[1658] Specific actions:

[1659] Users log in to the system and enter their work details and project history into an input form. Alternatively, they can upload existing data files (e.g., Excel or CSV files).

[1660] input:

[1661] Text data entered by the user or a CSV file uploaded by the user.

[1662] output:

[1663] The input data file transferred to the terminal.

[1664] Step 2:

[1665] The device checks the data format and content.

[1666] Specific actions:

[1667] The terminal checks the file format of the entered or uploaded data (whether it's CSV, Excel, etc.) and verifies that the content itself contains all required fields (e.g., project name, start date, end date). If there are any deficiencies in the format or content, an error message is displayed to the user.

[1668] input:

[1669] Data files received from the user.

[1670] output:

[1671] If the data format and content are correct, it will be marked as checked data. If there are errors in the format or content, an error message will be displayed.

[1672] Step 3:

[1673] The terminal sends the checked data to the server.

[1674] Specific actions:

[1675] The terminal encrypts the checked data and sends it to the server using a secure communication protocol (HTTPS). This ensures the security of the data.

[1676] input:

[1677] Checked data.

[1678] output:

[1679] The encrypted data is sent to the server.

[1680] Step 4:

[1681] The server analyzes the data it receives.

[1682] Specific actions:

[1683] The server receives the data and passes it to an analysis module (e.g., SpaCy, NLTK). The NLP module analyzes the data and extracts data points such as the type of work, project size, achievement level, and skills used.

[1684] input:

[1685] Encrypted data.

[1686] output:

[1687] Analyzed data points (e.g., type of work, project size, achievement level, skills used).

[1688] Step 5:

[1689] The server analyzes emotional information using emotion recognition technology.

[1690] Specific actions:

[1691] The server passes data acquired from the analysis module to an emotion recognition engine (e.g., IBM Watson, Microsoft Azure Text Analytics). The emotion engine analyzes the emotional tone of the text data (e.g., positive, negative, neutral) and evaluates the user's emotional state (e.g., satisfied, anxious, excited).

[1692] input:

[1693] Analyzed data points.

[1694] output:

[1695] Results of the assessment of emotional tone and emotional state.

[1696] Step 6:

[1697] The server automatically generates a report based on the analysis results and sentiment evaluation.

[1698] Specific actions:

[1699] The server launches a report generation module (e.g., JasperReports) and creates a report based on the analysis results and sentiment evaluation, including categories such as "Overall Performance," "Skill Analysis," "Sentiment Evaluation," and "Improvement Suggestions." The report is output in PDF or HTML format.

[1700] input:

[1701] Results of the assessment of emotional tone and emotional state.

[1702] output:

[1703] An automatically generated report (in PDF or HTML format).

[1704] Step 7:

[1705] The server sends the URL or file of the generated report to the terminal.

[1706] Specific actions:

[1707] The server re-encrypts the generated report file and sends it to the terminal via URL or file. HTTPS is used as the communication protocol.

[1708] input:

[1709] An automatically generated report.

[1710] output:

[1711] An encrypted report file is sent to the terminal.

[1712] Step 8:

[1713] The device presents the report to the user.

[1714] Specific actions:

[1715] The terminal decrypts the received report file and displays it on the user interface. Users can then review their work performance, skill evaluations, sentiment evaluations, and improvement suggestions.

[1716] input:

[1717] Encrypted report file.

[1718] output:

[1719] A report displayed in the user interface.

[1720] (Application Example 2)

[1721] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1722] Conventional business process inventory systems failed to take user emotions into account during analysis, making it impossible to understand users' emotional burdens and stress levels. Furthermore, in factory robot maintenance, there was a lack of feedback reflecting the emotional state of technicians, posing challenges to improving maintenance efficiency and technician satisfaction. This invention aims to solve these problems.

[1723] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1724] In this invention, the server includes means for the user to input or upload work details, means for the server to analyze the received data, means for the server to analyze emotional information using an emotion engine, means for generating an automatic report that includes maintenance history and the emotional evaluation of the technician, and means for the terminal to present the report to the user. This enables detailed feedback including emotional evaluation in addition to the user's work details, and in particular, enables the creation of efficient maintenance plans that reflect the emotional state of the technician in the maintenance of factory robots.

[1725] A "user" is an individual or group that uses the system to input work details or upload data.

[1726] "Job description" refers to the detailed information about the daily tasks and projects that users perform.

[1727] "Input" refers to the act of a user manually providing data to a system.

[1728] "Uploading" refers to the act of a user importing a pre-prepared file into the system.

[1729] "Means" refers to the methods or devices used to achieve a specific objective.

[1730] A "server" is a computer system that stores, processes, and analyzes data.

[1731] "Analysis" is the process of breaking down data into smaller parts and understanding and evaluating its contents.

[1732] An "emotion engine" is software or a system used to analyze the emotions behind text data obtained from users.

[1733] "Emotional information" refers to data that indicates the user's emotional state based on text data analyzed by the emotion engine.

[1734] An "automated report" is a report generated by the server based on analysis results and sentiment information.

[1735] "Maintenance history" refers to detailed records of maintenance work and repairs performed on factory robots.

[1736] A "technician" is a specialist who performs maintenance on factory robots.

[1737] "Presentation" refers to the act of showing or providing a generated report to the user.

[1738] This invention is a system that, in addition to supporting the inventory of work content, recognizes user emotions and provides feedback that reflects the analysis results. This system is realized through data exchange between the user, terminal, and server, as well as the use of an emotion engine.

[1739] System Configuration

[1740] 1. The user enters and uploads the details of their work.

[1741] Users enter their work details and maintenance history into an input form, or upload existing data files (e.g., Excel or CSV files). Users perform these operations after logging into a specific system.

[1742] The terminal receives input data and files from the user and checks their format and content.

[1743] 2. The server analyzes the data.

[1744] The terminal sends the received data to the server, and the server passes the received data to a module for analysis.

[1745] The server's natural language processing module (e.g., pandas, TextBlob) analyzes the maintenance history and extracts data points such as the type and frequency of work and the number of times parts were replaced. The server then uses an emotion engine to analyze sentiment information from comments written by users.

[1746] 3. Emotion recognition using an emotion engine

[1747] The server analyzes the emotional tone (e.g., positive, negative, neutral) from the text data of maintenance records and evaluates the emotional state of the technicians. The results of the emotion engine's analysis are stored in a database.

[1748] 4. The server automatically generates reports.

[1749] Based on the analysis results and the emotion evaluation by the emotion engine, the server automatically generates a report that includes maintenance history, frequency of parts replacement, and the emotion evaluation of the technicians. The report records in detail the maintenance status of each robot, the emotional state of the technicians, and the degree of wear and tear on the parts.

[1750] The server saves the generated report in PDF or HTML format and sends it to the terminal as needed.

[1751] 5. The device presents the report to the user.

[1752] The terminal displays the URL or file of the report received from the server in the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. Based on this information, future maintenance plans and training programs for technicians are developed.

[1753] Specific example

[1754] For example, if a technician comments, "There are too many parts replacements, and it's stressful," the emotion engine will rate this comment as a negative emotion. The server will then generate an automated report that includes this emotion rating and present the results to the technician and administrator to provide specific improvement suggestions and feedback.

[1755] Example of a prompt

[1756] Examples of prompt statements to input into a generative AI model include the following:

[1757] "Analyze the maintenance history of factory robots and evaluate the sentiment of the technicians. The data will include comments such as:

[1758] 'Too many parts need replacing, it's stressful.'

[1759] 'I was satisfied because the work went smoothly.'

[1760] 'I'm having trouble using the new tool.'

[1761] Based on these comments, please evaluate them as positive, negative, or neutral and reflect this in your report.

[1762] Based on this configuration, the system of the present invention efficiently organizes the user's work content and emotional state, and provides accurate feedback and emotional evaluation, enabling the creation of efficient maintenance plans that take into account the emotional information of technicians, particularly in the maintenance of factory robots.

[1763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1764] Step 1:

[1765] Users input and upload details of their work.

[1766] Users log in to the system and enter their work details and maintenance history into input forms, or upload existing data files (e.g., Excel or CSV files). Based on the input, the terminal checks the data format and content and saves it in the appropriate format. It verifies whether the input data format is correct and displays an error message if it is in an inappropriate format.

[1767] Step 2:

[1768] The device sends data to the server.

[1769] The terminal sends data entered or uploaded by the user to the server. Before sending data, it checks the data format and converts it to a format that the server can parse. The input data sent by the terminal to the server includes work content data and maintenance history data, while the output is formatted data.

[1770] Step 3:

[1771] The server analyzes the data.

[1772] The server passes the received data to an analysis module. Using a natural language processing module, the maintenance history is analyzed, and data points such as the type and frequency of work and the number of times parts were replaced are extracted. The input is data provided by the user, and the output is the analyzed data points. The server then performs a detailed analysis based on these points.

[1773] Step 4:

[1774] The server uses an emotion engine to analyze emotional information.

[1775] The server uses an emotion engine to analyze emotional information from user input and uploaded data. Specifically, it evaluates the emotional tone (positive, negative, neutral) of text data and analyzes the emotional state of the engineers. The input is text data including user comments, and the output is the analyzed emotional information.

[1776] Step 5:

[1777] The server automatically generates reports.

[1778] The server automatically generates a report based on the analysis results and the sentiment evaluation by the sentiment engine. The report includes maintenance history, frequency of parts replacement, and the technician's sentiment evaluation. The input is the analysis results up to the previous step, and the output is a document formatted as a report. This ensures that the maintenance status of the robot, the sentiment state of the technician, and the degree of wear and tear on the parts are recorded in detail.

[1779] Step 6:

[1780] The device presents the report to the user.

[1781] The terminal displays the URL or file of the report received from the server on the user interface. Users can view detailed information such as maintenance history, sentiment ratings, and areas for improvement. The input is the URL or file of the generated report, and the output is displayed on the user interface. Based on this information, users can develop future maintenance plans and training programs for technicians.

[1782] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1783] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1784] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1785] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1786] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1787] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1788] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1789] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1790] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1791] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1792] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1793] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1794] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1795] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1796] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1797] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1798] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1799] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1800] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1801] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1802] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1803] The following is further disclosed regarding the embodiments described above.

[1804] (Claim 1)

[1805] A means for users to input or upload work details,

[1806] A means for analyzing the data received by the server,

[1807] A means by which the server automatically generates a report based on the analysis results,

[1808] The means by which the terminal presents the report to the user,

[1809] A system that includes this.

[1810] (Claim 2)

[1811] The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

[1812] (Claim 3)

[1813] The system according to claim 1, further comprising means by which the server includes overall work performance, skill analysis, and improvement suggestions in an automated report.

[1814] "Example 1"

[1815] (Claim 1)

[1816] A means for users to input or upload work details,

[1817] A means for the terminal to check the format and content of the data received from the user,

[1818] A means by which the terminal sends verified data to the server,

[1819] A means for analyzing the data received by the server,

[1820] A means by which the server generates reports for each category based on the analysis results,

[1821] The server generates reports in PDF or HTML format,

[1822] A means for the terminal to display the generated report on the user interface,

[1823] A system that includes this.

[1824] (Claim 2)

[1825] The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

[1826] (Claim 3)

[1827] The system according to claim 1, further comprising means by which the server includes results categorized into categories in an automated report.

[1828] "Application Example 1"

[1829] (Claim 1)

[1830] A means for users to input or upload work details,

[1831] A means for analyzing the data received by the server,

[1832] A means by which the server automatically generates a report based on the analysis results,

[1833] The means by which the terminal presents the report to the user,

[1834] A means for the user to input the operation details by voice,

[1835] A method for sending data to a cloud server and analyzing it in real time,

[1836] A means of displaying the analysis results on the user interface,

[1837] A system that includes this.

[1838] (Claim 2)

[1839] The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

[1840] (Claim 3)

[1841] The system according to claim 1, further comprising means by which the server includes overall work performance, skill analysis, and improvement suggestions in an automated report.

[1842] "Example 2 of combining an emotion engine"

[1843] (Claim 1)

[1844] A means for users to input or upload work details,

[1845] The terminal has a means of checking the format and content of the data,

[1846] A means for the terminal to send checked data to the server,

[1847] A means for analyzing the data received by the server,

[1848] A server that uses emotion recognition technology to analyze emotional information,

[1849] A means by which the server automatically generates a report based on the analysis results and sentiment evaluation,

[1850] The means by which the terminal presents the report to the user,

[1851] A system that includes this.

[1852] (Claim 2)

[1853] The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

[1854] (Claim 3)

[1855] The system according to claim 1, further comprising means by which the server includes overall work performance, skill analysis, sentiment assessment, and improvement suggestions in an automated report.

[1856] "Application example 2 when combining with an emotional engine"

[1857] (Claim 1)

[1858] A means for users to input or upload work details,

[1859] A means for analyzing the data received by the server,

[1860] A means for a server to analyze emotional information using an emotion engine,

[1861] A means by which the server automatically generates a report based on the analysis results,

[1862] The means by which the terminal presents the report to the user,

[1863] A system that includes this.

[1864] (Claim 2)

[1865] The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

[1866] (Claim 3)

[1867] The system according to claim 1, further comprising means by which the server includes overall work performance, skill analysis, sentiment assessment, and improvement suggestions in an automated report.

[1868] (Claim 4)

[1869] The system according to claim 1, further comprising means for analyzing user-entered or uploaded maintenance history and performing a sentiment evaluation of the technician.

[1870] (Claim 5)

[1871] The system according to claim 1, further comprising means for including maintenance history, frequency of parts replacement, and technician sentiment ratings in the reports generated by the server. [Explanation of symbols]

[1872] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input or upload work details, A means for analyzing the data received by the server, A means by which the server automatically generates a report based on the analysis results, The means by which the terminal presents the report to the user, A system that includes this.

2. The system according to claim 1, further comprising means for the server to analyze business content data using natural language processing technology.

3. The system according to claim 1, further comprising means by which the server includes overall work performance, skill analysis, and improvement suggestions in an automated report.

Images