System

Abstract

A system is provided.SOLUTION: A system comprising: means for displaying a self-check sheet on a user device and receiving self-check data input by a user; means for analyzing the received self-check data; means for selecting appropriate training materials from a training materials database based on an analysis result and automatically generating a training program; means for packaging the generated training program and transmitting it to the user device; and means for periodically performing a skill check of the user and updating the training program based on the self-check data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Retail stores face the problem of staff feeling insecure about their customer service skills and lacking knowledge, which leads to a decline in service quality and high turnover rates. Conventional training methods make it difficult to provide training tailored to each staff member's skill level and concerns, and there is often a lack of training materials that allow staff to independently improve their skills. As a result, staff growth is hindered and the sustainability of self-learning is reduced. The present invention aims to solve these problems and achieve staff growth and improved staff retention rates. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for displaying a self-check sheet on a user device, receiving self-check data entered by the user, and analyzing the received self-check data. The system further includes a means for selecting appropriate training materials from a training materials database based on the analysis results, automatically generating a training program, and a means for packaging the generated training program and transmitting it to the user device. By periodically conducting user skill checks and providing a means for updating the training program based on the self-check data, the system supports staff skill improvement and continuous self-learning. The system can also analyze the self-check data using natural language processing to identify the user's skill level and concerns. Furthermore, the training program includes a variety of training materials, including videos, text, and quizzes, improving the quality of learning. This allows staff to efficiently improve their skills and resolve concerns, resulting in the provision of high-quality services and increased staff retention.

[0006] Yes, that's true. Below are definitions of key terms found in the claims:

[0007] "User device" is a general term for terminal devices used to display the self-check sheet and receive and study the training program.

[0008] A "self-check sheet" is a format containing questions and items for users to enter about their skill level, areas of concern, and what they would like to learn.

[0009] "Self-check data" refers to the information entered by the user into the self-check sheet.

[0010] The term "receiving means" refers to the function and process by which the server acquires the self-check data sent from the user device.

[0011] "Analysis means" refers to the technical methods and processes for analyzing the received self-check data and identifying the user's skill level and areas of concern.

[0012] A "training materials database" refers to a database that stores various training materials (videos, texts, quizzes, etc.) owned by a company.

[0013] "Means for selecting appropriate training materials" refers to the function and process of selecting training materials that meet the user's needs from the training materials database based on the analysis results.

[0014] "Training Program" refers to a set of learning content that compiles selected training materials for the purpose of improving a User's skills.

[0015] "Means for automatic generation" refers to the functions and processes for automatically configuring and packaging training programs based on the analysis results.

[0016] "Packaging means" refers to the functions and processes that integrate selected training materials into a user-accessible format.

[0017] "Transmission means" refers to the technical methods and functions for transmitting the generated training program to a user device.

[0018] "Skill Check" refers to an assessment tool for measuring a user's current skill level and mastery.

[0019] "Means for updating" refers to the functions and processes for providing a new training program appropriate to the user's latest skill level based on the results of the skill check.

[0020] "Video, text, and quizzes" refers to the content formats used as training materials, with videos intended for visual and auditory learning, text for reading comprehension learning, and quizzes for checking comprehension and solidifying knowledge.

[0021] "Natural language processing" refers to a technical technique for analyzing text data entered by a user using machine learning algorithms to understand its meaning. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention provides a system for supporting independent learning for retail store staff with the aim of improving their skills and alleviating anxiety. The system is implemented using a user device, a server, and a database.

[0044] System configuration

[0045] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. The server receives and analyzes the self-check data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[0046] Program processing

[0047] 1. Display and fill out the self-check sheet

[0048] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[0049] 2. Sending self-check data

[0050] The terminal transmits the self-check data entered by the user to the server.

[0051] 3. Analysis of self-check data

[0052] The server analyzes the received self-check data and identifies the user's skill level and concerns using natural language processing (NLP) technology.

[0053] 4. Selection of training materials and creation of training programs

[0054] Based on the analysis results, the server selects training materials that meet the user's needs from a training materials database and automatically generates a personalized training program. The selected training materials include videos, texts, and quizzes.

[0055] 5. Send and study the training program

[0056] The server again transmits the generated training program to the terminal, and the user starts learning on the terminal.

[0057] 6. Regular skill checks and training program updates

[0058] The server periodically sends skill check notifications to the terminal, and the user re-enters the self-check sheet according to the notifications. The system re-analyzes the new self-check data and updates the training program as necessary.

[0059] Specific examples

[0060] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[0061] 1. Self-check

[0062] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0063] 2. Data transmission and analysis

[0064] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[0065] 3. Selecting teaching materials and creating programs

[0066] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of training materials and automatically generates a training program that includes these.

[0067] 4. Send and study the training program

[0068] The server sends the generated training program to the terminal, and the user (Staff A) uses it to start learning.

[0069] 5. Regular skill checks and program updates

[0070] The server sends a skill check notification to the terminal one month later, and the user (Staff A) fills in the self-check sheet again. Based on this new data, the training program is updated as necessary.

[0071] In this way, the present invention is a system that can efficiently support users in improving their skills and eliminating their anxieties, thereby improving the quality of service and increasing staff retention rates.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[0075] Step 2:

[0076] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[0077] Step 3:

[0078] The device collects the entered self-check data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0079] Step 4:

[0080] The server stores the received self-check data in a database, then uses natural language processing (NLP) to analyze the data and identify the user's skill level, concerns, and learning goals.

[0081] Step 5:

[0082] Based on the analysis results, the server selects appropriate training materials from a training materials database using a matching algorithm that takes into account the user's skill level and concerns.

[0083] Step 6:

[0084] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[0085] Step 7:

[0086] The server packages the generated training program and transmits it again to the terminal.

[0087] Step 8:

[0088] Users can access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[0089] Step 9:

[0090] The server notifies the user at regular intervals that a skill check will be conducted, and the user fills out the self-check sheet again.

[0091] Step 10:

[0092] The device collects new self-check data and sends it to the server, which then re-analyzes the data and updates the training program as the user progresses. This update involves selecting new learning materials and regenerating the program.

[0093] The above steps realize a system that continuously supports users in improving their skills and self-learning.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Retail store staff need personalized training and instruction to improve their skills and resolve concerns about their work. However, traditional training methods have the problem of being unable to respond to individual needs and provide effective learning support. It is also difficult to grasp in a timely manner the level of skills staff possess and the concerns they have. For this reason, there is a need for an efficient training system that can improve staff motivation and increase staff retention rates.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for displaying a self-check sheet on an information processing device and receiving self-check data entered by a user, means for analyzing the received self-check data, and means for selecting appropriate educational materials from an educational material database based on the analysis results and automatically generating an educational program, thereby enabling efficient and personalized support for improving users' skills and resolving their anxieties.

[0099] An "information processing device" is a device for displaying the self-check sheet and accepting input from the user, and includes, for example, a tablet, PC, smartphone, etc.

[0100] The "self-check sheet" is an electronic form in which users can enter their skill level, concerns, and what they would like to learn.

[0101] "Self-check data" refers to information entered by the user into the self-check sheet, including skill level, areas of concern, and desired learning content.

[0102] The "server" is a computer system that receives and analyzes self-check data and generates and transmits educational programs.

[0103] "Analysis" refers to identifying the user's skill level and areas of concern based on the received self-check data, and includes the use of natural language processing technology.

[0104] An "educational materials database" is a database that stores educational materials in various formats, such as videos, documents, and tests.

[0105] An "educational program" is a training course that is automatically generated based on the analysis results to meet the user's needs, and includes selected educational materials.

[0106] "Packaging" means assembling the generated educational program in a form that is easy for users to use.

[0107] "Periodic" means repeated at regular intervals or on a schedule.

[0108] A "skill check" is a process that involves users completing and submitting a self-check sheet to reassess their skill level and any areas of concern.

[0109] "Natural language processing" is a technology that allows computers to understand and process human language, and is used to analyze self-check data.

[0110] The present invention is a system that supports independent learning by retail store staff with the aim of improving their own skills and relieving anxiety, and is implemented using a user device (terminal), a server, and a database.

[0111] System configuration

[0112] The system mainly consists of the following components:

[0113] 1. User Device (Terminal)

[0114] Example: Tablet, PC, Smartphone

[0115] Role: Displaying the self-check sheet and accepting user input

[0116] 2. Server

[0117] Role: Receiving data, analyzing, generating and sending educational programs

[0118] 3. Database

[0119] Example: Educational materials database

[0120] Role: Preservation and selection of educational materials

[0121] Program processing

[0122] Hardware and Software Use

[0123] User device (terminal): Displays the self-check sheet and sends the data entered by the user to the server. For example, a dedicated application is run on a tablet.

[0124] Server: Applying natural language processing (NLP) techniques to analyze the received data. Based on the analysis results, it selects appropriate educational materials from a database of educational materials and generates a personalized educational program using a generative AI model.

[0125] Database: Stores educational materials (videos, documents, tests, etc.) and provides the necessary materials in response to requests from the server.

[0126] Specific examples

[0127] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[0128] 1. Self-check

[0129] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0130] 2. Data transmission and analysis

[0131] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[0132] 3. Selecting teaching materials and creating programs

[0133] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of educational materials and automatically generates an educational program that includes these.

[0134] 4. Send and study the training program

[0135] The server sends the generated educational program to the terminal, and the user (Staff A) uses it to begin learning.

[0136] Prompt Sentence Examples

[0137] Use the following prompt for your generative AI model:

[0138] "Generate a training program based on the following information. For a user who has entered that they want to learn more about customer service skill deficiencies and cleaning procedures, suggest appropriate training materials and a training program based on that."

[0139] In this way, the present invention provides an efficient and personalized educational program that meets the needs of the user, thereby improving skills and eliminating concerns.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Display and fill out the self-check sheet

[0142] The server generates a self-check sheet at regular intervals or based on a specific event, and transmits it to the terminal.

[0143] Input: Trigger for self-check sheet generation by the server

[0144] Output: Send self-check sheet to terminal

[0145] The terminal displays the sent self-check sheet to the user.

[0146] Input: Self-check sheet sent from the server

[0147] Output: Self-check sheet displayed on the terminal screen

[0148] The user enters their skill level, concerns, and what they want to learn into the self-check sheet.

[0149] Input: User's skill level, concerns, and learning goals

[0150] Output: Data entry into self-check sheet

[0151] Specific actions: For example, enter "3 / 5" in the "Customer service skills" field, "Lack of product knowledge" in the "Concerns" field, and "Features of new products" in the "Things you want to learn" field.

[0152] Step 2: Submitting self-check data

[0153] The terminal transmits the self-check data entered by the user to the server.

[0154] Input: Self-check data entered by the user

[0155] Output: Self-check data sent to the server

[0156] Specific operation: When the input information is confirmed, press the data send button, and the following data will be sent to the server: "Customer service skills: 3 / 5", "Concerns: Lack of product knowledge", "Things to learn: Features of new products".

[0157] Step 3: Analyzing the self-check data

[0158] The server analyzes the received self-check data using natural language processing (NLP) technology.

[0159] Input: Received self-check data

[0160] Output: Identification of user skill level and concerns

[0161] Specific operation: For example, the NLP engine extracts keywords such as "customer service skills" and "product knowledge" and classifies them into their respective categories.

[0162] Step 4: Select training materials and create a training program

[0163] The server selects training materials that meet the user's needs from the training materials database based on the analysis results, and the selected training materials include videos, texts, and quizzes.

[0164] Input: Analysis results (user's skill level and concerns)

[0165] Output: List of selected training materials

[0166] Specific operation: For example, for a user who lacks customer service skills, "videos on customer service etiquette" and "basic responses" are selected.

[0167] The server generates a personalized training program based on the selected teaching materials.

[0168] Input: List of selected training materials

[0169] Output: Generated training program

[0170] Specific operations: For example, automatically generate a program that involves watching a video tutorial on customer service etiquette, then browsing a new product catalog, and finally checking comprehension with a simple quiz.

[0171] Step 5: Submit and study the training program

[0172] The server transmits the generated training program to the terminal.

[0173] Input: Generated training program

[0174] Output: Sends training program to terminal

[0175] The terminal notifies the user of the received training program and displays a learning screen.

[0176] Input: Training program sent from the server

[0177] Output: Notify the user and display the learning screen

[0178] The user follows the on-screen instructions and proceeds with their studies using the designated training materials.

[0179] Input: Training instructions from the terminal

[0180] Output: Learning progress

[0181] Specific operation: For example, the device screen displays "Please watch a video on customer service etiquette," and the user presses the play button to watch the video.

[0182] Step 6: Regularly check skills and update training programs

[0183] After the user has continued learning for a certain period of time, the server sends a notification to the terminal requesting a periodic skill check.

[0184] Input: A period of time has passed

[0185] Output: Skill check request notification to the device

[0186] The terminal displays the skill check sheet again to the user and prompts for input.

[0187] Input: Skill check request notification from the server

[0188] Output: Display of skill check sheet

[0189] The user then enters their new skill level and any concerns into the self-check sheet again.

[0190] Input: User's new skill level, anxiety

[0191] Output: Re-enter data into the self-check sheet

[0192] The server reanalyzes the data based on the new self-check and updates the training program as necessary.

[0193] Input: New self-check data

[0194] Output: Updated training program

[0195] Specific operation: For example, after one month, input "improvement status of customer service skills" and "new concerns." As a result, a training program incorporating additional materials and new quizzes will be provided in addition to the old program.

[0196] (Application example 1)

[0197] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0198] The problem that this invention aims to solve is to provide a system that allows store staff to study to efficiently improve their skills in between work. Conventional systems have made it difficult to provide personalized study programs tailored to each staff member's skill level and concerns, and to ensure that staff have time to study while on the job. As a result, staff skill improvement is limited, and the quality of service tends to decline.

[0199] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0200] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data, means for selecting appropriate learning materials from a learning database based on the analysis results and automatically generating a learning program, means for packaging the generated learning program and transmitting it to the user device, means for periodically checking the user's skills and updating the learning program based on the self-check data, and means for being installed on a smartphone and allowing staff to efficiently study between work. This enables individual staff to efficiently carry out personalized learning according to their skill level and areas of concern, improving the quality of service and improving staff skills.

[0201] A "user device" is an electronic device used by a user, specifically a smartphone, tablet, PC, etc.

[0202] A "self-check sheet" is a questionnaire in which users enter their own skills and concerns.

[0203] "Self-check data" refers to the information entered by the user into the self-check sheet, and is data related to skill level and areas of concern.

[0204] "Server" refers to a computer system connected to a network that receives, analyzes, stores, and transmits data.

[0205] "Means for analysis" refers to the technology or algorithms used to analyze the received self-check data and understand its contents.

[0206] A "learning database" refers to a collection of data that stores training materials and information.

[0207] "Learning materials" are educational content provided to users, including videos, texts, quizzes, and the like.

[0208] "Learning Program" means an educational plan or course that combines selected learning materials to improve a User's skills.

[0209] "Packaging" refers to organizing the generated learning program into a single unit and making it in a form that can be delivered to a user device.

[0210] "Skill Check" refers to a periodic assessment method for evaluating a user's skill level.

[0211] "Means of being installed on a smartphone" refers to the placement and configuration of software so that the learning support system can run on a smartphone.

[0212] "Means for efficient learning between work tasks" refers to methods and techniques that allow users to efficiently carry out learning activities in between their regular work tasks.

[0213] The present invention is a system that links a user device (such as a smartphone or tablet), a server, and a learning database to enable store staff to efficiently improve their skills in between work.

[0214] System configuration

[0215] This system consists of the following components:

[0216] 1. User Device:

[0217] A device on which users fill out self-check sheets and receive and display learning programs. Specifically, this includes smartphones and tablets.

[0218] 2. Server:

[0219] It is the central system for receiving, analyzing, storing data, and generating and transmitting learning programs. The server has the following specific functions:

[0220] Regularly sending self-check sheets

[0221] Receiving and saving self-check data

[0222] Data analysis using natural language processing (NLP) techniques (e.g., Google® Cloud Natural Language API)

[0223] Management of learning databases (e.g., MySQL (registered trademark))

[0224] Generate and send learning programs tailored to the user

[0225] 3. Learning database:

[0226] This is a database that stores educational materials (videos, texts, quizzes, etc.) to help users improve their skills.

[0227] Operation flow

[0228] Self-check and data transmission

[0229] The user (store staff) inputs the necessary information for the self-check sheet on the user device, such as "improving customer service skills" and "concerns about handling complaints." This data is sent from the user device to the server.

[0230] Data analysis and learning program generation

[0231] The server analyzes the received self-check data using natural language processing technology to identify the user's skill level and areas of concern, then selects appropriate learning materials from a learning database (e.g., video tutorials on customer service techniques, detailed manuals on handling complaints, etc.) and automatically generates a learning program optimized for the user.

[0232] Submitting and Viewing Learning Programs

[0233] The generated learning program is then sent back to the user device, and the user uses it to study. The user device can study to improve their skills between work tasks through the displayed learning program.

[0234] Skills check and program update

[0235] The server periodically sends a skill check notification to the user device, and the user fills in the self-check sheet again. Re-analysis is performed based on the new data, and the learning program is updated as necessary.

[0236] Specific examples

[0237] A specific example is Staff A at a certain store. Staff A uses his smartphone to input his "lack of customer service skills" and "concerns about handling complaints." The server analyzes this information using natural language processing technology, and generates a learning program from a learning database that includes a "video tutorial on customer service skills" and a "detailed manual on handling complaints," and sends it to Staff A's smartphone.

[0238] Prompt Sentence Examples

[0239] "What kind of materials should I provide to Staff A to help him improve his skills?"

[0240] This allows users to study efficiently while at work, improving their skills and alleviating anxiety.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] Display and enter information on the self-check sheet

[0244] The user device (smartphone) displays the self-check sheet sent from the server, and the user inputs information such as their skill level and concerns into the self-check sheet.

[0245] Input: Data entered by the user into the self-check sheet (e.g., lack of customer service skills, concerns about handling complaints)

[0246] Output: Self-check data sent from the user device

[0247] Step 2:

[0248] Sending self-check data

[0249] The user device sends the entered self-check data to the server, which transfers the data using a secure communication protocol (e.g., HTTPS).

[0250] Input: Self-check data entered by the user

[0251] Output: Self-check data sent to the server

[0252] Step 3:

[0253] Analysis of self-check data

[0254] The server analyzes the received self-check data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to analyze the text data and identify the user's skill level and areas of concern.

[0255] Input: Self-check data sent to the server

[0256] Output: Analyzed data (user skill level, identified concerns)

[0257] Step 4:

[0258] Selection of learning materials and creation of learning programs

[0259] Based on the analysis results, the server selects appropriate learning materials from the learning database and automatically generates a personalized learning program by extracting relevant videos, texts, and quizzes from a MySQL database.

[0260] Input: Parsed data

[0261] Output: The generated personalized learning program

[0262] Step 5:

[0263] Submitting and Viewing Learning Programs

[0264] The server transmits the generated learning program to the user device, which displays the program and allows the user to begin learning.

[0265] Input: Generated personalized learning program

[0266] Output: The learning program sent to the user device

[0267] Step 6:

[0268] Regular skill checks and learning program updates

[0269] The server periodically sends skill check notifications to the user device. The user re-enters the self-check sheet, and the server analyzes the new data and updates the learning program as necessary.

[0270] Input: Newly entered self-check data

[0271] Output: Updated learning program

[0272] Step 7:

[0273] Efficient learning in between work

[0274] Users can use their smartphones to study in between work hours, following the learning programs that have been sent to them. Specifically, they can use their free time or breaks to work on the learning materials.

[0275] Input: Learning activities according to the learning program

[0276] Output: Improved skill level and reduced anxiety

[0277] By following the specific steps, users can improve their skills efficiently and enhance the quality of their work.

[0278] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0279] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and by combining it with an emotion engine, provides effective training that also takes into account the emotional state of the user. The system is implemented using a user device, a server, a database, and an emotion engine.

[0280] System configuration

[0281] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc. The server receives and analyzes the self-check data and emotional data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[0282] Program processing

[0283] 1. Display and fill out the self-check sheet

[0284] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[0285] 2. Collecting Emotional Data

[0286] The device collects emotional data from the user's facial expressions, tone of voice, and text input. This is handled by the emotion engine.

[0287] 3. Sending self-check data and emotion data

[0288] The device sends the input self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[0289] 4. Data Analysis

[0290] The server stores the received self-check data and emotion data in a database, then analyzes the self-check data using natural language processing (NLP) and uses the emotion data to identify the user's skill level, anxiety factors, and emotional state.

[0291] 5. Selection of training materials and creation of training programs

[0292] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state using a matching algorithm that takes into account the user's skill level, anxiety factors, and emotional state.

[0293] 6. Send and study training programs

[0294] The server then packages the generated training program and sends it back to the device, where the user begins learning. They improve their skills by watching videos, reading texts, and answering quizzes.

[0295] 7. Regular skill checks and training program updates

[0296] The server periodically notifies the user when a skill check is due. The user then fills out the self-check sheet again, and their emotional data is updated. Based on the new self-check and emotional data, the training program is updated as needed. This update involves selecting new teaching materials and regenerating the program.

[0297] Specific examples

[0298] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, consider that Staff A is feeling stressed during training.

[0299] 1. Self-check

[0300] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0301] 2. Collecting Emotional Data

[0302] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice, collecting information that indicates he is feeling stressed.

[0303] 3. Data transmission and analysis

[0304] The device sends self-check data and emotion data to a server, which then analyzes the received information using natural language processing technology and an emotion analysis engine.

[0305] 4. Selection of teaching materials and program creation

[0306] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[0307] 5. Send and study the training program

[0308] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[0309] 6. Regular skill checks and emotional data updates

[0310] The server notifies the user (Staff A) to periodically check their skills and re-collect their emotional data, and the user fills out the self-check sheet again, updating their emotional data.

[0311] In this way, the present invention is a system that can improve users' skills, eliminate their anxieties, and provide efficient training that takes into account their emotional state, thereby improving the quality of service and increasing staff retention rates.

[0312] The processing flow will be explained below.

[0313] Step 1:

[0314] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[0315] Step 2:

[0316] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[0317] Step 3:

[0318] The device uses an emotion engine to collect emotional data from the user's facial expressions, tone of voice, and text input, including stress levels, motivation, and more.

[0319] Step 4:

[0320] The device sends the entered self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[0321] Step 5:

[0322] The server stores the received self-check data and emotion data in a database.

[0323] Step 6:

[0324] The server uses natural language processing (NLP) to analyze the self-check data and identify the user's skill level and anxiety factors. In parallel, it uses a sentiment analysis engine to analyze the user's emotional state (e.g., stress level, motivation).

[0325] Step 7:

[0326] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state. For example, for users with high stress levels, the server may consider adding videos on relaxation techniques.

[0327] Step 8:

[0328] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[0329] Step 9:

[0330] The server packages the generated training program and transmits it again to the terminal.

[0331] Step 10:

[0332] Users access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[0333] Step 11:

[0334] The server periodically notifies the user that a skill check will be conducted. The user then fills out the self-check sheet again and updates their emotional data.

[0335] Step 12:

[0336] The terminal collects new self-check data and emotion data and transmits them to the server.

[0337] Step 13:

[0338] The server analyzes the new data and updates the training program as needed, which includes selecting new materials and regenerating the program.

[0339] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, Staff A is experiencing stress during training.

[0340] 1. Self-check

[0341] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0342] 2. Collecting Emotional Data

[0343] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice and recognizes that Staff A is feeling stressed.

[0344] 3. Data transmission and analysis

[0345] The device sends self-check data and emotion data to a server, which then analyzes the received information using natural language processing technology and an emotion analysis engine.

[0346] 4. Selection of teaching materials and program creation

[0347] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[0348] 5. Send and study the training program

[0349] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[0350] 6. Regular skill checks and emotional data updates

[0351] The server notifies the user (Staff A) to periodically check their skills and re-collect their emotional data, and the user fills out the self-check sheet again, updating their emotional data.

[0352] This series of steps allows for more effective training programs to be provided based on detailed data, including the user's emotional state, via the emotion engine, helping to improve staff skills, alleviate anxiety, and even manage stress.

[0353] Example 2

[0354] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0355] While conventional training systems provide training programs based on the user's skill level and learning needs, they lack the ability to provide training programs that take into account the user's emotional state. This makes it difficult to provide effective training while reducing user stress and anxiety. Furthermore, the process for reflecting the results of periodic skill checks in the training program is insufficient, preventing continuous learning effects.

[0356] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0357] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data and the user's emotional data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and recollecting their emotional data and updating the training program based on the self-check data and emotional data. This makes it possible to provide a personalized training program that takes into account the user's emotional state and corresponds to their skill level and anxiety factors.

[0358] "User device" refers to a terminal device used by a user, which has the function of displaying a self-check sheet and inputting data.

[0359] A "self-check sheet" is an input form that allows users to self-evaluate their skill level, learning needs, and areas of concern.

[0360] "Self-check data" refers to information that a user inputs into a self-check sheet via a user device.

[0361] "Emotional data" refers to data about a user's emotional state analyzed from facial expressions, tone of voice, text input, and the like.

[0362] "Server" refers to a central processing unit capable of analyzing data received from user devices and generating and transmitting appropriate training programs.

[0363] A "training materials database" refers to a database that stores training materials (videos, texts, quizzes, etc.) used in training.

[0364] A "training program" refers to a package of learning content that is automatically generated based on the user's skill level, concerns, and emotional state.

[0365] "Natural language processing (NLP)" refers to the technology that enables computers to understand and analyze human language.

[0366] A "matching algorithm" refers to a calculation method for selecting the most appropriate training materials based on user input data and analysis data.

[0367] "Encryption Technology" refers to the technology used to communicate data securely (e.g., HTTPS).

[0368] System Overview

[0369] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and provides effective training that takes into account the emotional state of the user by combining an emotion engine. This system is implemented using a user device, a server, a database, and an emotion engine.

[0370] Hardware and software used

[0371] User device (e.g., tablet, PC, smartphone): Displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc.

[0372] Server: Receives and analyzes the self-check data and emotion data sent from the user device. Furthermore, based on the analysis results, it has the function of selecting appropriate training materials from a training materials database and automatically generating a personalized training program.

[0373] Database: The training materials database stores a variety of training materials including videos, texts, and quizzes.

[0374] Emotion engine: Software for collecting and analyzing emotional data from users' facial expressions, tone of voice, and text input.

[0375] Hardware and software operation

[0376] 1. Display and fill out the self-check sheet

[0377] The server periodically sends a self-check sheet to the user device, which includes items for the user to fill in, such as their skill level, what they want to learn, and any concerns they may have.

[0378] The user enters his / her own condition into this self-check sheet.

[0379] 2. Collecting Emotional Data

[0380] The user device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, which are then analyzed by the emotion engine and output as numerical data.

[0381] 3. Sending self-check data and emotion data

[0382] The user device transmits the self-check data and emotion data entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[0383] 4. Data Analysis

[0384] The server stores the received self-check data and emotion data, analyzes the data using natural language processing (NLP), and identifies the user's skill level and emotional state based on the analysis results.

[0385] 5. Selection of training materials and creation of training programs

[0386] Based on the analysis results, the server selects training materials from the training materials database that match the user's needs and emotional state, using a matching algorithm.

[0387] The server automatically generates a personalized training program by combining the selected teaching materials.

[0388] 6. Send and study training programs

[0389] The server packages the generated training program and transmits it back to the user device.

[0390] Users can view training programs on their devices and progress through their studies, which include watching videos, reading texts, and answering quizzes.

[0391] 7. Regular skill checks and training program updates

[0392] The server periodically sends notifications to the user to check skills and re-collect emotion data.

[0393] The user fills in the self-check sheet again and updates the emotion data.

[0394] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[0395] Examples of concrete examples and prompt sentence usage

[0396] As a concrete example, consider a case where a retail store staff member wants to improve their customer service skills but feels they lack knowledge about cleaning procedures. They enter "lack of customer service skills" and "I would like to learn more about cleaning procedures" into a self-check sheet on a terminal. The user's facial expressions and tone of voice indicate that they are feeling stressed. This data is sent to a server for analysis. Based on the analysis results, a training program is generated that includes a video to reinforce customer service skills, a detailed manual on cleaning procedures, and a video on stress reduction, and sent to the terminal.

[0397] An example prompt is:

[0398] "Generate explanatory text that takes into account sentiment data to guide the process of generating training programs for retail store staff who feel they lack customer service skills or are unsure about cleaning procedures."

[0399] The above is an embodiment of the present invention.

[0400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0401] Step 1:

[0402] The server transmits a self-check sheet to the user device every Monday at 9:00 a.m. The sheet includes items for filling in skill level, areas of concern, and content that the user wants to learn.

[0403] Input: Recurring Schedule

[0404] Output: Self-check sheet displayed on the user's device

[0405] Step 2:

[0406] Users enter details of their own situation, such as "lack of customer service skills" or "want to learn cleaning procedures," into the self-check sheet displayed on the device.

[0407] Input: Self-check sheet

[0408] Output: Input self-check data

[0409] Step 3:

[0410] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time and transmits them to the emotion engine, which analyzes them and generates emotion data.

[0411] Input: User facial expressions, tone of voice, and text input

[0412] Output: Emotion data

[0413] Step 4:

[0414] The device transmits the self-check data entered by the user and the emotion data generated by the emotion engine to the server using a secure communication protocol (e.g., HTTPS).

[0415] Input: Self-check data and emotion data

[0416] Output: Data sent to the server

[0417] Step 5:

[0418] The server stores the received self-check data and emotion data, analyzes them using natural language processing (NLP), and identifies the user's skill level, anxiety factors, and emotional state based on the analysis results.

[0419] Input: Self-check data and emotion data

[0420] Output: Skill level, anxiety, and emotional state analysis results

[0421] Step 6:

[0422] Based on the analysis results, the server selects appropriate training materials from a database using a matching algorithm to extract materials that match the user's skill level, concerns, and emotional state.

[0423] Input: Analysis results

[0424] Output: Selected training materials

[0425] Step 7:

[0426] The server automatically generates a personalized training program by combining the selected learning materials, taking into account the user's skill level and emotional state.

[0427] Input: Selected training materials

[0428] Output: Personalized training program

[0429] Step 8:

[0430] The server then packages the generated training program and sends it back to the user's device. The user then views the training program on their device and progresses through the learning process, which includes watching videos, reading texts, and answering quizzes.

[0431] Input: Generated training program

[0432] Output: Packaged training program sent to user device

[0433] Step 9:

[0434] The server sends a notification to the user on the first day of each month to re-collect the skill check and emotional data. The user then fills out the self-check sheet again and updates the emotional data.

[0435] Input: Recurring Schedule

[0436] Output: Recollected self-check data and emotion data

[0437] Step 10:

[0438] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[0439] Input: Recollected self-check data and emotion data

[0440] Output: Updated training program

[0441] (Application example 2)

[0442] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0443] Traditional training systems often fail to provide sufficient support for staff skill development and anxiety relief. Furthermore, because they do not take into account the emotional state of staff, the training content may be inappropriate. Furthermore, regular skill checks are not conducted, which can delay long-term growth. This limits the effectiveness of training, making it difficult to improve staff retention and service quality.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for displaying a self-check sheet on the user device and receiving self-check data entered by the user, means including an emotion engine for collecting emotion data from the user's facial expressions and tone of voice, means for analyzing the received and collected self-check data and emotion data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and updating the training program based on the self-check data and emotion data. This makes it possible to improve the user's skills and provide effective training that takes into account the user's emotional state.

[0445] definition statement

[0446] A "user device" is an electronic device that a user uses to fill out a self-check sheet or study a training program, and includes devices such as smartphones, tablets, and personal computers.

[0447] The "self-check sheet" is a questionnaire in which users can enter information about their skill level, areas of concern, and what they would like to learn.

[0448] "Self-check data" refers to data entered by the user into the self-check sheet, and includes the user's skill level, areas of concern, and desired learning content.

[0449] "Emotion data" refers to data relating to emotions collected from the user's facial expressions and tone of voice.

[0450] An "emotion engine" is software or hardware that analyzes a user's facial expressions and tone of voice to collect emotional data.

[0451] The "analysis means" has the function of analyzing the received and collected self-check data and emotion data, and uses techniques such as natural language processing.

[0452] The "Training Materials Database" is a database that stores various training materials, including video tutorials, text materials, quizzes, and the like.

[0453] A "training program" is an educational program automatically generated by combining training materials selected from a training material database based on the analysis results.

[0454] "Packaging" refers to assembling the generated training program into a format that can be sent to a user device.

[0455] A "skill check" is a check that a user performs periodically to reassess their skill level.

[0456] "Natural language processing" is a computer technology that analyzes text data entered by a user and extracts and classifies information based on that data.

[0457] MODE FOR CARRYING OUT THE INVENTION

[0458] The system implemented based on this invention is a support tool for retail store staff to learn independently, improve their skills, and alleviate their anxiety. The overall system configuration is as follows:

[0459] System configuration

[0460] The system includes a user device, a server, a database, and an emotion engine.

[0461] 1. User Device

[0462] The user device is used by the user to transmit the information entered in the self-check sheet and receive the training program, and typically includes a smartphone, tablet, or personal computer.

[0463] 2. Server

[0464] The server receives and analyzes the self-check data and emotion data sent from the user device, using natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[0465] 3. Database

[0466] The database stores training materials, which may be in various formats such as video tutorials, text materials, and quizzes.

[0467] 4. Emotion Engine

[0468] The emotion engine is designed to collect emotional data from the user's facial expressions and tone of voice. The system uses emotion recognition software such as DeepFace.

[0469] Program processing

[0470] The server first receives the self-check data sent from the user device, and then receives the emotion data collected through the camera and microphone installed on the user device. These data are transmitted using a secure communication protocol (e.g., HTTPS).

[0471] The received data is analyzed within the server. Natural language processing technology is used for the analysis to identify the user's skill level, concerns, and emotional state. For example, if a user enters "I would like to improve my customer service skills" into a self-check sheet and emotional data indicates a high stress level, appropriate training materials are selected based on this information.

[0472] The server then selects training materials from the training materials database that match the user's needs and emotional state, and automatically generates a personalized training program, which is then sent back to the user's device to enable the user to study.

[0473] Specific examples

[0474] Consider the example of a retail store staff member who wants to improve their customer service skills. The staff member uses their smartphone to fill out a self-checklist, writing, "I lack customer service skills," and "I would like to learn more about cleaning procedures." Next, the smartphone's camera and microphone are used to collect facial expressions and tone of voice, which are then analyzed by an emotion engine. The emotional data reveals that the staff member is feeling stressed during the training.

[0475] This data is then sent to a server, which analyzes it and generates training programs that include video tutorials on customer service techniques, detailed cleaning procedures, and stress reduction videos. Through this process, the training users receive becomes more effective and personalized.

[0476] Prompt Sentence Examples

[0477] "Enter your current skill level (on a scale of 0-5). Next, indicate your current stress level as high, medium, or low. Finally, enter the topic you would like to learn about.

[0478] Example: Skill level: 4, Stress level: High, Topic to learn: Customer service etiquette.

[0479] This system allows users to receive the necessary training at the appropriate time, improving their skills and emotional state.

[0480] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0481] Program processing steps

[0482] Step 1:

[0483] The user fills out a self-check sheet displayed on the user device (smartphone or tablet). Specifically, the user enters their skill level, areas of concern, and topics they would like to learn. The entered data is stored as self-check data.

[0484] input

[0485] Your skill level, stress level, and topics you want to learn

[0486] output

[0487] Self-check data

[0488] Data processing and calculation

[0489] Format the input data into a data structure such as JSON.

[0490] Step 2:

[0491] The device captures the user's facial expressions with a camera and collects the tone of voice with a microphone, and uses an emotion engine (e.g., DeepFace) to analyze the user's emotional state from these data.

[0492] input

[0493] Captured facial expression images and voice data

[0494] output

[0495] Emotional Data

[0496] Data processing and calculation

[0497] DeepFace and other emotion recognition software analyze images and audio to generate emotion data.

[0498] Step 3:

[0499] The device transmits the collected self-check data and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[0500] input

[0501] Self-check data, emotion data

[0502] output

[0503] Data sent to the server

[0504] Data processing and calculation

[0505] Data is encrypted and transmitted securely using HTTPS.

[0506] Step 4:

[0507] The server analyzes the received self-check data and emotion data, and uses natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[0508] input

[0509] Self-check data, emotion data

[0510] output

[0511] User skill level, fears, and emotional state

[0512] Data processing and calculation

[0513] Using natural language processing technology, text data is analyzed to identify skill levels and areas of concern.

[0514] Analyze the emotion data to determine the current emotional state.

[0515] Step 5:

[0516] Based on the analysis results, the server selects appropriate training materials from a training materials database and automatically generates a personalized training program.

[0517] input

[0518] User skill level, fears, and emotional state

[0519] Training materials database

[0520] output

[0521] Personalized Training Programs

[0522] Data processing and calculation

[0523] A matching algorithm is used to select the most appropriate training materials based on the analysis results.

[0524] A training program is created based on the selected teaching materials.

[0525] Step 6:

[0526] The server packages the generated training program and transmits it back to the user device.

[0527] input

[0528] Personalized Training Programs

[0529] output

[0530] Training program sent to user device

[0531] Data processing and calculation

[0532] The training program is packaged in an appropriate format so that it can be sent to the user's device.

[0533] Step 7:

[0534] Users run the training program on their device and study, improving their skills by watching videos, reading texts, and answering quizzes.

[0535] input

[0536] Training Program

[0537] output

[0538] Learning progress data

[0539] Data processing and calculation

[0540] Skill checks and emotional data are updated according to learning progress.

[0541] Step 8:

[0542] The server periodically notifies the user to take a skill check, collects new self-check data and emotion data from the user, and updates the training program based on the new data.

[0543] input

[0544] New self-check data and emotion data

[0545] output

[0546] Updated Training Program

[0547] Data processing and calculation

[0548] Compare and analyze new data with previous data and regenerate and update training programs as needed.

[0549] This process step allows users to receive ongoing appropriate training, improving their skills and emotional state.

[0550] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0551] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0552] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0553] [Second embodiment]

[0554] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0555] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0556] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0557] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0558] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0559] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0560] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0561] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0562] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0563] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0564] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0565] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0566] The present invention provides a system for supporting independent learning for retail store staff with the aim of improving their skills and alleviating anxiety. The system is implemented using a user device, a server, and a database.

[0567] System configuration

[0568] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. The server receives and analyzes the self-check data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[0569] Program processing

[0570] 1. Display and fill out the self-check sheet

[0571] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[0572] 2. Sending self-check data

[0573] The terminal transmits the self-check data entered by the user to the server.

[0574] 3. Analysis of self-check data

[0575] The server analyzes the received self-check data and identifies the user's skill level and concerns using natural language processing (NLP) technology.

[0576] 4. Selection of training materials and creation of training programs

[0577] Based on the analysis results, the server selects training materials that meet the user's needs from a training materials database and automatically generates a personalized training program. The selected training materials include videos, texts, and quizzes.

[0578] 5. Send and study the training program

[0579] The server again transmits the generated training program to the terminal, and the user starts learning on the terminal.

[0580] 6. Regular skill checks and training program updates

[0581] The server periodically sends skill check notifications to the terminal, and the user re-enters the self-check sheet according to the notifications. The system re-analyzes the new self-check data and updates the training program as necessary.

[0582] Specific examples

[0583] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[0584] 1. Self-check

[0585] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0586] 2. Data transmission and analysis

[0587] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[0588] 3. Selecting teaching materials and creating programs

[0589] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of training materials and automatically generates a training program that includes these.

[0590] 4. Send and study the training program

[0591] The server sends the generated training program to the terminal, and the user (Staff A) uses it to start learning.

[0592] 5. Regular skill checks and program updates

[0593] The server sends a skill check notification to the terminal one month later, and the user (Staff A) fills in the self-check sheet again. Based on this new data, the training program is updated as necessary.

[0594] In this way, the present invention is a system that can efficiently support users in improving their skills and eliminating their anxieties, thereby improving the quality of service and increasing staff retention rates.

[0595] The processing flow will be explained below.

[0596] Step 1:

[0597] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[0598] Step 2:

[0599] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[0600] Step 3:

[0601] The device collects the entered self-check data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0602] Step 4:

[0603] The server stores the received self-check data in a database, then uses natural language processing (NLP) to analyze the data and identify the user's skill level, concerns, and learning goals.

[0604] Step 5:

[0605] Based on the analysis results, the server selects appropriate training materials from a training materials database using a matching algorithm that takes into account the user's skill level and concerns.

[0606] Step 6:

[0607] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[0608] Step 7:

[0609] The server packages the generated training program and transmits it again to the terminal.

[0610] Step 8:

[0611] Users can access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[0612] Step 9:

[0613] The server notifies the user at regular intervals that a skill check will be conducted, and the user fills out the self-check sheet again.

[0614] Step 10:

[0615] The device collects new self-check data and sends it to the server, which then reanalyzes the data and updates the training program as the user progresses. This update involves selecting new learning materials and regenerating the program.

[0616] The above steps realize a system that continuously supports users in improving their skills and self-learning.

[0617] Example 1

[0618] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] Retail store staff need personalized training and instruction to improve their skills and resolve concerns about their work. However, traditional training methods have the problem of being unable to respond to individual needs and provide effective learning support. It is also difficult to grasp in a timely manner the level of skills staff possess and the concerns they have. For this reason, there is a need for an efficient training system that can improve staff motivation and increase staff retention rates.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0621] In this invention, the server includes means for displaying a self-check sheet on an information processing device and receiving self-check data entered by a user, means for analyzing the received self-check data, and means for selecting appropriate educational materials from an educational material database based on the analysis results and automatically generating an educational program, thereby enabling efficient and personalized support for improving users' skills and resolving their anxieties.

[0622] An "information processing device" is a device for displaying the self-check sheet and accepting input from the user, and includes, for example, a tablet, PC, smartphone, etc.

[0623] The "self-check sheet" is an electronic form in which users can enter their skill level, concerns, and what they would like to learn.

[0624] "Self-check data" refers to information entered by the user into the self-check sheet, including skill level, areas of concern, and desired learning content.

[0625] The "server" is a computer system that receives and analyzes self-check data and generates and transmits educational programs.

[0626] "Analysis" refers to identifying the user's skill level and areas of concern based on the received self-check data, and includes the use of natural language processing technology.

[0627] An "educational materials database" is a database that stores educational materials in various formats, such as videos, documents, and tests.

[0628] An "educational program" is a training course that is automatically generated based on the analysis results to meet the user's needs, and includes selected educational materials.

[0629] "Packaging" means assembling the generated educational program in a form that is easy for users to use.

[0630] "Periodic" means repeated at regular intervals or on a schedule.

[0631] A "skill check" is a process that involves users completing and submitting a self-check sheet to reassess their skill level and any areas of concern.

[0632] "Natural language processing" is a technology that allows computers to understand and process human language, and is used to analyze self-check data.

[0633] The present invention is a system that supports independent learning by retail store staff with the aim of improving their own skills and relieving anxiety, and is implemented using a user device (terminal), a server, and a database.

[0634] System configuration

[0635] The system mainly consists of the following components:

[0636] 1. User Device (Terminal)

[0637] Example: Tablet, PC, Smartphone

[0638] Role: Displaying the self-check sheet and accepting user input

[0639] 2. Server

[0640] Role: Receiving data, analyzing, generating and sending educational programs

[0641] 3. Database

[0642] Example: Educational materials database

[0643] Role: Preservation and selection of educational materials

[0644] Program processing

[0645] Hardware and Software Use

[0646] User device (terminal): Displays the self-check sheet and sends the data entered by the user to the server. For example, a dedicated application is run on a tablet.

[0647] Server: Applying natural language processing (NLP) techniques to analyze the received data. Based on the analysis results, it selects appropriate educational materials from a database of educational materials and generates a personalized educational program using a generative AI model.

[0648] Database: Stores educational materials (videos, documents, tests, etc.) and provides the necessary materials in response to requests from the server.

[0649] Specific examples

[0650] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[0651] 1. Self-check

[0652] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0653] 2. Data transmission and analysis

[0654] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[0655] 3. Selecting teaching materials and creating programs

[0656] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of educational materials and automatically generates an educational program that includes these.

[0657] 4. Send and study the training program

[0658] The server sends the generated educational program to the terminal, and the user (Staff A) uses it to begin learning.

[0659] Prompt Sentence Examples

[0660] Use the following prompt for your generative AI model:

[0661] "Generate a training program based on the following information. For a user who has entered that they want to learn more about customer service skill deficiencies and cleaning procedures, suggest appropriate training materials and a training program based on that."

[0662] In this way, the present invention provides an efficient and personalized educational program that meets the needs of the user, thereby improving skills and eliminating concerns.

[0663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0664] Step 1: Display and fill out the self-check sheet

[0665] The server generates a self-check sheet at regular intervals or based on a specific event, and transmits it to the terminal.

[0666] Input: Trigger for self-check sheet generation by the server

[0667] Output: Send self-check sheet to terminal

[0668] The terminal displays the sent self-check sheet to the user.

[0669] Input: Self-check sheet sent from the server

[0670] Output: Self-check sheet displayed on the terminal screen

[0671] The user enters their skill level, concerns, and what they want to learn into the self-check sheet.

[0672] Input: User's skill level, concerns, and learning goals

[0673] Output: Data entry into self-check sheet

[0674] Specific actions: For example, enter "3 / 5" in the "Customer service skills" field, "Lack of product knowledge" in the "Concerns" field, and "Features of new products" in the "Things you want to learn" field.

[0675] Step 2: Submitting self-check data

[0676] The terminal transmits the self-check data entered by the user to the server.

[0677] Input: Self-check data entered by the user

[0678] Output: Self-check data sent to the server

[0679] Specific operation: When the input information is confirmed, press the data send button, and the following data will be sent to the server: "Customer service skills: 3 / 5", "Concerns: Lack of product knowledge", "Things to learn: Features of new products".

[0680] Step 3: Analyzing the self-check data

[0681] The server analyzes the received self-check data using natural language processing (NLP) technology.

[0682] Input: Received self-check data

[0683] Output: Identification of user skill level and concerns

[0684] Specific operation: For example, the NLP engine extracts keywords such as "customer service skills" and "product knowledge" and classifies them into their respective categories.

[0685] Step 4: Select training materials and create a training program

[0686] The server selects training materials that meet the user's needs from the training materials database based on the analysis results, and the selected training materials include videos, texts, and quizzes.

[0687] Input: Analysis results (user's skill level and concerns)

[0688] Output: List of selected training materials

[0689] Specific operation: For example, for a user who lacks customer service skills, "videos on customer service etiquette" and "basic responses" are selected.

[0690] The server generates a personalized training program based on the selected teaching materials.

[0691] Input: List of selected training materials

[0692] Output: Generated training program

[0693] Specific operations: For example, automatically generate a program that involves watching a video tutorial on customer service etiquette, then browsing a new product catalog, and finally checking comprehension with a simple quiz.

[0694] Step 5: Submit and study the training program

[0695] The server transmits the generated training program to the terminal.

[0696] Input: Generated training program

[0697] Output: Sends training program to terminal

[0698] The terminal notifies the user of the received training program and displays a learning screen.

[0699] Input: Training program sent from the server

[0700] Output: Notify the user and display the learning screen

[0701] The user follows the on-screen instructions and proceeds with their studies using the designated training materials.

[0702] Input: Training instructions from the terminal

[0703] Output: Learning progress

[0704] Specific operation: For example, the device screen displays "Please watch a video on customer service etiquette," and the user presses the play button to watch the video.

[0705] Step 6: Regularly check skills and update training programs

[0706] After the user has continued learning for a certain period of time, the server sends a notification to the terminal requesting a periodic skill check.

[0707] Input: A period of time has passed

[0708] Output: Skill check request notification to the device

[0709] The terminal displays the skill check sheet again to the user and prompts for input.

[0710] Input: Skill check request notification from the server

[0711] Output: Display of skill check sheet

[0712] The user then enters their new skill level and any concerns into the self-check sheet again.

[0713] Input: User's new skill level, anxiety

[0714] Output: Re-enter data into the self-check sheet

[0715] The server reanalyzes the data based on the new self-check and updates the training program as necessary.

[0716] Input: New self-check data

[0717] Output: Updated training program

[0718] Specific operation: For example, after one month, input "improvement status of customer service skills" and "new concerns." As a result, a training program incorporating additional materials and new quizzes will be provided in addition to the old program.

[0719] (Application example 1)

[0720] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0721] The problem that this invention aims to solve is to provide a system that allows store staff to study to efficiently improve their skills in between work. Conventional systems have made it difficult to provide personalized study programs tailored to each staff member's skill level and concerns, and to ensure that staff have time to study while on the job. As a result, staff skill improvement is limited, and the quality of service tends to decline.

[0722] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0723] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data, means for selecting appropriate learning materials from a learning database based on the analysis results and automatically generating a learning program, means for packaging the generated learning program and transmitting it to the user device, means for periodically checking the user's skills and updating the learning program based on the self-check data, and means for being installed on a smartphone and allowing staff to efficiently study between work. This enables individual staff to efficiently carry out personalized learning according to their skill level and areas of concern, improving the quality of service and improving staff skills.

[0724] A "user device" is an electronic device used by a user, specifically a smartphone, tablet, PC, etc.

[0725] A "self-check sheet" is a questionnaire in which users enter their own skills and concerns.

[0726] "Self-check data" refers to the information entered by the user into the self-check sheet, and is data related to skill level and areas of concern.

[0727] "Server" refers to a computer system connected to a network that receives, analyzes, stores, and transmits data.

[0728] "Means for analysis" refers to the technology or algorithms used to analyze the received self-check data and understand its contents.

[0729] A "learning database" refers to a collection of data that stores training materials and information.

[0730] "Learning materials" are educational content provided to users, including videos, texts, quizzes, and the like.

[0731] "Learning Program" means an educational plan or course that combines selected learning materials to improve a User's skills.

[0732] "Packaging" refers to organizing the generated learning program into a single unit and making it in a form that can be delivered to a user device.

[0733] "Skill Check" refers to a periodic assessment method for evaluating a user's skill level.

[0734] "Means of being installed on a smartphone" refers to the placement and configuration of software so that the learning support system can run on a smartphone.

[0735] "Means for efficient learning between work tasks" refers to methods and techniques that allow users to efficiently carry out learning activities in between their regular work tasks.

[0736] The present invention is a system that links a user device (such as a smartphone or tablet), a server, and a learning database to enable store staff to efficiently improve their skills in between work.

[0737] System configuration

[0738] This system consists of the following components:

[0739] 1. User Device:

[0740] A device on which users fill out self-check sheets and receive and display learning programs. Specifically, this includes smartphones and tablets.

[0741] 2. Server:

[0742] It is the central system for receiving, analyzing, storing data, and generating and transmitting learning programs. The server has the following specific functions:

[0743] Regularly sending self-check sheets

[0744] Receiving and saving self-check data

[0745] Data analysis using natural language processing (NLP) techniques (e.g., Google Cloud Natural Language API)

[0746] Management of learning databases (e.g. MySQL)

[0747] Generate and send learning programs tailored to the user

[0748] 3. Learning database:

[0749] This is a database that stores educational materials (videos, texts, quizzes, etc.) to help users improve their skills.

[0750] Operation flow

[0751] Self-check and data transmission

[0752] The user (store staff) inputs the necessary information for the self-check sheet on the user device, such as "improving customer service skills" and "concerns about handling complaints." This data is sent from the user device to the server.

[0753] Data analysis and learning program generation

[0754] The server analyzes the received self-check data using natural language processing technology to identify the user's skill level and areas of concern, then selects appropriate learning materials from a learning database (e.g., video tutorials on customer service techniques, detailed manuals on handling complaints, etc.) and automatically generates a learning program optimized for the user.

[0755] Submitting and Viewing Learning Programs

[0756] The generated learning program is then sent back to the user device, and the user uses it to study. The user device can study to improve their skills between work tasks through the displayed learning program.

[0757] Skills check and program update

[0758] The server periodically sends a skill check notification to the user device, and the user fills in the self-check sheet again. Re-analysis is performed based on the new data, and the learning program is updated as necessary.

[0759] Specific examples

[0760] A specific example is Staff A at a certain store. Staff A uses his smartphone to input his "lack of customer service skills" and "concerns about handling complaints." The server analyzes this information using natural language processing technology, and generates a learning program from a learning database that includes a "video tutorial on customer service skills" and a "detailed manual on handling complaints," and sends it to Staff A's smartphone.

[0761] Prompt Sentence Examples

[0762] "What kind of materials should I provide to Staff A to help him improve his skills?"

[0763] This allows users to study efficiently while at work, improving their skills and alleviating anxiety.

[0764] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0765] Step 1:

[0766] Display and enter information on the self-check sheet

[0767] The user device (smartphone) displays the self-check sheet sent from the server, and the user inputs information such as their skill level and concerns into the self-check sheet.

[0768] Input: Data entered by the user into the self-check sheet (e.g., lack of customer service skills, concerns about handling complaints)

[0769] Output: Self-check data sent from the user device

[0770] Step 2:

[0771] Sending self-check data

[0772] The user device sends the entered self-check data to the server, which transfers the data using a secure communication protocol (e.g., HTTPS).

[0773] Input: Self-check data entered by the user

[0774] Output: Self-check data sent to the server

[0775] Step 3:

[0776] Analysis of self-check data

[0777] The server analyzes the received self-check data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to analyze the text data and identify the user's skill level and areas of concern.

[0778] Input: Self-check data sent to the server

[0779] Output: Analyzed data (user skill level, identified concerns)

[0780] Step 4:

[0781] Selection of learning materials and creation of learning programs

[0782] Based on the analysis results, the server selects appropriate learning materials from the learning database and automatically generates a personalized learning program by extracting relevant videos, texts, and quizzes from the MySQL database.

[0783] Input: Parsed data

[0784] Output: The generated personalized learning program

[0785] Step 5:

[0786] Submitting and Viewing Learning Programs

[0787] The server transmits the generated learning program to the user device, which displays the program and allows the user to begin learning.

[0788] Input: Generated personalized learning program

[0789] Output: The learning program sent to the user device

[0790] Step 6:

[0791] Regular skill checks and learning program updates

[0792] The server periodically sends skill check notifications to the user device. The user re-enters the self-check sheet, and the server analyzes the new data and updates the learning program as necessary.

[0793] Input: Newly entered self-check data

[0794] Output: Updated learning program

[0795] Step 7:

[0796] Efficient learning in between work

[0797] Users can use their smartphones to study in between work hours, following the learning programs that have been sent to them. Specifically, they can use their free time or breaks to work on the learning materials.

[0798] Input: Learning activities according to the learning program

[0799] Output: Improved skill level and reduced anxiety

[0800] By following the specific steps, users can improve their skills efficiently and enhance the quality of their work.

[0801] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0802] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and by combining it with an emotion engine, provides effective training that also takes into account the emotional state of the user. The system is implemented using a user device, a server, a database, and an emotion engine.

[0803] System configuration

[0804] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc. The server receives and analyzes the self-check data and emotional data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[0805] Program processing

[0806] 1. Display and fill out the self-check sheet

[0807] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[0808] 2. Collecting Emotional Data

[0809] The device collects emotional data from the user's facial expressions, tone of voice, and text input. This is handled by the emotion engine.

[0810] 3. Sending self-check data and emotion data

[0811] The device sends the input self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[0812] 4. Data Analysis

[0813] The server stores the received self-check data and emotion data in a database, then analyzes the self-check data using natural language processing (NLP) and uses the emotion data to identify the user's skill level, anxiety factors, and emotional state.

[0814] 5. Selection of training materials and creation of training programs

[0815] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state using a matching algorithm that takes into account the user's skill level, anxiety factors, and emotional state.

[0816] 6. Send and study training programs

[0817] The server then packages the generated training program and sends it back to the device, where the user begins learning. They improve their skills by watching videos, reading texts, and answering quizzes.

[0818] 7. Regular skill checks and training program updates

[0819] The server periodically notifies the user when a skill check is due. The user then fills out the self-check sheet again, and their emotional data is updated. Based on the new self-check and emotional data, the training program is updated as needed. This update involves selecting new teaching materials and regenerating the program.

[0820] Specific examples

[0821] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, consider that Staff A is feeling stressed during training.

[0822] 1. Self-check

[0823] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0824] 2. Collecting Emotional Data

[0825] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice, collecting information that indicates he is feeling stressed.

[0826] 3. Data transmission and analysis

[0827] The device sends self-check data and emotion data to a server, which then analyzes the received information using natural language processing technology and an emotion analysis engine.

[0828] 4. Selection of teaching materials and program creation

[0829] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[0830] 5. Send and study the training program

[0831] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[0832] 6. Regular skill checks and emotional data updates

[0833] The server notifies the user (Staff A) to periodically check their skills and re-collect their emotional data, and the user fills out the self-check sheet again, updating their emotional data.

[0834] In this way, the present invention is a system that can improve users' skills, eliminate their anxieties, and provide efficient training that takes into account their emotional state, thereby improving the quality of service and increasing staff retention rates.

[0835] The processing flow will be explained below.

[0836] Step 1:

[0837] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[0838] Step 2:

[0839] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[0840] Step 3:

[0841] The device uses an emotion engine to collect emotional data from the user's facial expressions, tone of voice, and text input, including stress levels, motivation, and more.

[0842] Step 4:

[0843] The device sends the entered self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[0844] Step 5:

[0845] The server stores the received self-check data and emotion data in a database.

[0846] Step 6:

[0847] The server uses natural language processing (NLP) to analyze the self-check data and identify the user's skill level and anxiety factors. In parallel, it uses a sentiment analysis engine to analyze the user's emotional state (e.g., stress level, motivation).

[0848] Step 7:

[0849] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state. For example, for users with high stress levels, the server may consider adding videos on relaxation techniques.

[0850] Step 8:

[0851] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[0852] Step 9:

[0853] The server packages the generated training program and transmits it again to the terminal.

[0854] Step 10:

[0855] Users access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[0856] Step 11:

[0857] The server periodically notifies the user that a skill check will be conducted. The user then fills out the self-check sheet again and updates their emotional data.

[0858] Step 12:

[0859] The terminal collects new self-check data and emotion data and transmits them to the server.

[0860] Step 13:

[0861] The server analyzes the new data and updates the training program as needed, which includes selecting new materials and regenerating the program.

[0862] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, Staff A is experiencing stress during training.

[0863] 1. Self-check

[0864] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[0865] 2. Collecting Emotional Data

[0866] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice and recognizes that Staff A is feeling stressed.

[0867] 3. Data transmission and analysis

[0868] The device sends self-check data and emotion data to a server, which then analyzes the received information using natural language processing technology and an emotion analysis engine.

[0869] 4. Selection of teaching materials and program creation

[0870] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[0871] 5. Send and study the training program

[0872] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[0873] 6. Regular skill checks and emotional data updates

[0874] The server notifies the user (Staff A) to periodically check their skills and re-collect their emotional data, and the user fills out the self-check sheet again, updating their emotional data.

[0875] This series of steps allows for more effective training programs to be provided based on detailed data, including the user's emotional state, via the emotion engine, helping to improve staff skills, alleviate anxiety, and even manage stress.

[0876] Example 2

[0877] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0878] While conventional training systems provide training programs based on the user's skill level and learning needs, they lack the ability to provide training programs that take into account the user's emotional state. This makes it difficult to provide effective training while reducing user stress and anxiety. Furthermore, the process for reflecting the results of periodic skill checks in the training program is insufficient, preventing continuous learning effects.

[0879] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0880] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data and the user's emotional data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and recollecting their emotional data and updating the training program based on the self-check data and emotional data. This makes it possible to provide a personalized training program that takes into account the user's emotional state and corresponds to their skill level and anxiety factors.

[0881] "User device" refers to a terminal device used by a user, which has the function of displaying a self-check sheet and inputting data.

[0882] A "self-check sheet" is an input form that allows users to self-evaluate their skill level, learning needs, and areas of concern.

[0883] "Self-check data" refers to information that a user inputs into a self-check sheet via a user device.

[0884] "Emotional data" refers to data about a user's emotional state analyzed from facial expressions, tone of voice, text input, and the like.

[0885] "Server" refers to a central processing unit capable of analyzing data received from user devices and generating and transmitting appropriate training programs.

[0886] A "training materials database" refers to a database that stores training materials (videos, texts, quizzes, etc.) used in training.

[0887] A "training program" refers to a package of learning content that is automatically generated based on the user's skill level, concerns, and emotional state.

[0888] "Natural language processing (NLP)" refers to the technology that enables computers to understand and analyze human language.

[0889] A "matching algorithm" refers to a calculation method for selecting the most appropriate training materials based on user input data and analysis data.

[0890] "Encryption Technology" refers to the technology used to communicate data securely (e.g., HTTPS).

[0891] System Overview

[0892] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and provides effective training that takes into account the emotional state of the user by combining an emotion engine. This system is implemented using a user device, a server, a database, and an emotion engine.

[0893] Hardware and software used

[0894] User device (e.g., tablet, PC, smartphone): Displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc.

[0895] Server: Receives and analyzes the self-check data and emotion data sent from the user device. Furthermore, based on the analysis results, it has the function of selecting appropriate training materials from a training materials database and automatically generating a personalized training program.

[0896] Database: The training materials database stores a variety of training materials including videos, texts, and quizzes.

[0897] Emotion engine: Software for collecting and analyzing emotional data from users' facial expressions, tone of voice, and text input.

[0898] Hardware and software operation

[0899] 1. Display and fill out the self-check sheet

[0900] The server periodically sends a self-check sheet to the user device, which includes items for the user to fill in, such as their skill level, what they want to learn, and any concerns they may have.

[0901] The user enters his / her own condition into this self-check sheet.

[0902] 2. Collecting Emotional Data

[0903] The user device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, which are then analyzed by the emotion engine and output as numerical data.

[0904] 3. Sending self-check data and emotion data

[0905] The user device transmits the self-check data and emotion data entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[0906] 4. Data Analysis

[0907] The server stores the received self-check data and emotion data, analyzes the data using natural language processing (NLP), and identifies the user's skill level and emotional state based on the analysis results.

[0908] 5. Selection of training materials and creation of training programs

[0909] Based on the analysis results, the server selects training materials from the training materials database that match the user's needs and emotional state, using a matching algorithm.

[0910] The server automatically generates a personalized training program by combining the selected teaching materials.

[0911] 6. Send and study training programs

[0912] The server packages the generated training program and transmits it back to the user device.

[0913] Users can view training programs on their devices and progress through their studies, which include watching videos, reading texts, and answering quizzes.

[0914] 7. Regular skill checks and training program updates

[0915] The server periodically sends notifications to the user to check skills and re-collect emotion data.

[0916] The user fills in the self-check sheet again and updates the emotion data.

[0917] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[0918] Examples of concrete examples and prompt sentence usage

[0919] As a concrete example, consider a case where a retail store staff member wants to improve their customer service skills but feels they lack knowledge about cleaning procedures. They enter "lack of customer service skills" and "I would like to learn more about cleaning procedures" into a self-check sheet on a terminal. The user's facial expressions and tone of voice indicate that they are feeling stressed. This data is sent to a server for analysis. Based on the analysis results, a training program is generated that includes a video to reinforce customer service skills, a detailed manual on cleaning procedures, and a video on stress reduction, and sent to the terminal.

[0920] An example prompt is:

[0921] "Generate explanatory text that takes into account sentiment data to guide the process of generating training programs for retail store staff who feel they lack customer service skills or are unsure about cleaning procedures."

[0922] The above is an embodiment of the present invention.

[0923] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0924] Step 1:

[0925] The server transmits a self-check sheet to the user device every Monday at 9:00 a.m. The sheet includes items for filling in skill level, areas of concern, and content that the user wants to learn.

[0926] Input: Recurring Schedule

[0927] Output: Self-check sheet displayed on the user's device

[0928] Step 2:

[0929] Users enter details of their own situation, such as "lack of customer service skills" or "want to learn cleaning procedures," into the self-check sheet displayed on the device.

[0930] Input: Self-check sheet

[0931] Output: Input self-check data

[0932] Step 3:

[0933] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time and transmits them to the emotion engine, which analyzes them and generates emotion data.

[0934] Input: User facial expressions, tone of voice, and text input

[0935] Output: Emotion data

[0936] Step 4:

[0937] The device transmits the self-check data entered by the user and the emotion data generated by the emotion engine to the server using a secure communication protocol (e.g., HTTPS).

[0938] Input: Self-check data and emotion data

[0939] Output: Data sent to the server

[0940] Step 5:

[0941] The server stores the received self-check data and emotion data, analyzes them using natural language processing (NLP), and identifies the user's skill level, anxiety factors, and emotional state based on the analysis results.

[0942] Input: Self-check data and emotion data

[0943] Output: Skill level, anxiety, and emotional state analysis results

[0944] Step 6:

[0945] Based on the analysis results, the server selects appropriate training materials from a database using a matching algorithm to extract materials that match the user's skill level, concerns, and emotional state.

[0946] Input: Analysis results

[0947] Output: Selected training materials

[0948] Step 7:

[0949] The server automatically generates a personalized training program by combining the selected learning materials, taking into account the user's skill level and emotional state.

[0950] Input: Selected training materials

[0951] Output: Personalized training program

[0952] Step 8:

[0953] The server then packages the generated training program and sends it back to the user's device. The user then views the training program on their device and progresses through the learning process, which includes watching videos, reading texts, and answering quizzes.

[0954] Input: Generated training program

[0955] Output: Packaged training program sent to user device

[0956] Step 9:

[0957] The server sends a notification to the user on the first day of each month to re-collect the skill check and emotional data. The user then fills out the self-check sheet again and updates the emotional data.

[0958] Input: Recurring Schedule

[0959] Output: Recollected self-check data and emotion data

[0960] Step 10:

[0961] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[0962] Input: Recollected self-check data and emotion data

[0963] Output: Updated training program

[0964] (Application example 2)

[0965] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0966] Traditional training systems often fail to provide sufficient support for staff skill development and anxiety relief. Furthermore, because they do not take into account the emotional state of staff, the training content may be inappropriate. Furthermore, regular skill checks are not conducted, which can delay long-term growth. This limits the effectiveness of training, making it difficult to improve staff retention and service quality.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for displaying a self-check sheet on the user device and receiving self-check data entered by the user, means including an emotion engine for collecting emotion data from the user's facial expressions and tone of voice, means for analyzing the received and collected self-check data and emotion data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and updating the training program based on the self-check data and emotion data. This makes it possible to improve the user's skills and provide effective training that takes into account the user's emotional state.

[0968] definition statement

[0969] A "user device" is an electronic device that a user uses to fill out a self-check sheet or study a training program, and includes devices such as smartphones, tablets, and personal computers.

[0970] The "self-check sheet" is a questionnaire in which users can enter information about their skill level, areas of concern, and what they would like to learn.

[0971] "Self-check data" refers to data entered by the user into the self-check sheet, and includes the user's skill level, areas of concern, and desired learning content.

[0972] "Emotion data" refers to data relating to emotions collected from the user's facial expressions and tone of voice.

[0973] An "emotion engine" is software or hardware that analyzes a user's facial expressions and tone of voice to collect emotional data.

[0974] The "analysis means" has the function of analyzing the received and collected self-check data and emotion data, and uses techniques such as natural language processing.

[0975] The "Training Materials Database" is a database that stores various training materials, including video tutorials, text materials, quizzes, and the like.

[0976] A "training program" is an educational program automatically generated by combining training materials selected from a training material database based on the analysis results.

[0977] "Packaging" refers to assembling the generated training program into a format that can be sent to a user device.

[0978] A "skill check" is a check that a user performs periodically to reassess their skill level.

[0979] "Natural language processing" is a computer technology that analyzes text data entered by a user and extracts and classifies information based on that data.

[0980] MODE FOR CARRYING OUT THE INVENTION

[0981] The system implemented based on this invention is a support tool for retail store staff to learn independently, improve their skills, and alleviate their anxiety. The overall system configuration is as follows:

[0982] System configuration

[0983] The system includes a user device, a server, a database, and an emotion engine.

[0984] 1. User Device

[0985] The user device is used by the user to transmit the information entered in the self-check sheet and receive the training program, and typically includes a smartphone, tablet, or personal computer.

[0986] 2. Server

[0987] The server receives and analyzes the self-check data and emotion data sent from the user device, using natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[0988] 3. Database

[0989] The database stores training materials, which may be in various formats such as video tutorials, text materials, and quizzes.

[0990] 4. Emotion Engine

[0991] The emotion engine is designed to collect emotional data from the user's facial expressions and tone of voice. The system uses emotion recognition software such as DeepFace.

[0992] Program processing

[0993] The server first receives the self-check data sent from the user device, and then receives the emotion data collected through the camera and microphone installed on the user device. These data are transmitted using a secure communication protocol (e.g., HTTPS).

[0994] The received data is analyzed within the server. Natural language processing technology is used for the analysis to identify the user's skill level, concerns, and emotional state. For example, if a user enters "I would like to improve my customer service skills" into a self-check sheet and emotional data indicates a high stress level, appropriate training materials are selected based on this information.

[0995] The server then selects training materials from the training materials database that match the user's needs and emotional state, and automatically generates a personalized training program, which is then sent back to the user's device to enable the user to study.

[0996] Specific examples

[0997] Consider the example of a retail store staff member who wants to improve their customer service skills. The staff member uses their smartphone to fill out a self-checklist, writing, "I lack customer service skills," and "I would like to learn more about cleaning procedures." Next, the smartphone's camera and microphone are used to collect facial expressions and tone of voice, which are then analyzed by an emotion engine. The emotional data reveals that the staff member is feeling stressed during the training.

[0998] This data is then sent to a server, which analyzes it and generates training programs that include video tutorials on customer service techniques, detailed cleaning procedures, and stress reduction videos. Through this process, the training users receive becomes more effective and personalized.

[0999] Prompt Sentence Examples

[1000] "Enter your current skill level (on a scale of 0-5). Next, indicate your current stress level as high, medium, or low. Finally, enter the topic you would like to learn about.

[1001] Example: Skill level: 4, Stress level: High, Topic to learn: Customer service etiquette.

[1002] This system allows users to receive the necessary training at the appropriate time, improving their skills and emotional state.

[1003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1004] Program processing steps

[1005] Step 1:

[1006] The user fills out a self-check sheet displayed on the user device (smartphone or tablet). Specifically, the user enters their skill level, areas of concern, and topics they would like to learn. The entered data is stored as self-check data.

[1007] input

[1008] Your skill level, stress level, and topics you want to learn

[1009] output

[1010] Self-check data

[1011] Data processing and calculation

[1012] Format the input data into a data structure such as JSON.

[1013] Step 2:

[1014] The device captures the user's facial expressions with a camera and collects the tone of voice with a microphone, and uses an emotion engine (e.g., DeepFace) to analyze the user's emotional state from these data.

[1015] input

[1016] Captured facial expression images and voice data

[1017] output

[1018] Emotional Data

[1019] Data processing and calculation

[1020] DeepFace and other emotion recognition software analyze images and audio to generate emotion data.

[1021] Step 3:

[1022] The device transmits the collected self-check data and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1023] input

[1024] Self-check data, emotion data

[1025] output

[1026] Data sent to the server

[1027] Data processing and calculation

[1028] Data is encrypted and transmitted securely using HTTPS.

[1029] Step 4:

[1030] The server analyzes the received self-check data and emotion data, and uses natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[1031] input

[1032] Self-check data, emotion data

[1033] output

[1034] User skill level, fears, and emotional state

[1035] Data processing and calculation

[1036] Using natural language processing technology, text data is analyzed to identify skill levels and areas of concern.

[1037] Analyze the emotion data to determine the current emotional state.

[1038] Step 5:

[1039] Based on the analysis results, the server selects appropriate training materials from a training materials database and automatically generates a personalized training program.

[1040] input

[1041] User skill level, fears, and emotional state

[1042] Training materials database

[1043] output

[1044] Personalized Training Programs

[1045] Data processing and calculation

[1046] A matching algorithm is used to select the most appropriate training materials based on the analysis results.

[1047] A training program is created based on the selected teaching materials.

[1048] Step 6:

[1049] The server packages the generated training program and transmits it back to the user device.

[1050] input

[1051] Personalized Training Programs

[1052] output

[1053] Training program sent to user device

[1054] Data processing and calculation

[1055] The training program is packaged in an appropriate format so that it can be sent to the user's device.

[1056] Step 7:

[1057] Users run the training program on their device and study, improving their skills by watching videos, reading texts, and answering quizzes.

[1058] input

[1059] Training Program

[1060] output

[1061] Learning progress data

[1062] Data processing and calculation

[1063] Skill checks and emotional data are updated according to learning progress.

[1064] Step 8:

[1065] The server periodically notifies the user to take a skill check, collects new self-check data and emotion data from the user, and updates the training program based on the new data.

[1066] input

[1067] New self-check data and emotion data

[1068] output

[1069] Updated Training Program

[1070] Data processing and calculation

[1071] Compare and analyze new data with previous data and regenerate and update training programs as needed.

[1072] This process step allows users to receive ongoing appropriate training, improving their skills and emotional state.

[1073] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1075] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1076] [Third embodiment]

[1077] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1078] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1080] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1081] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1084] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1085] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1087] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1088] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1089] The present invention provides a system for supporting independent learning for retail store staff with the aim of improving their skills and alleviating anxiety. The system is implemented using a user device, a server, and a database.

[1090] System configuration

[1091] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. The server receives and analyzes the self-check data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[1092] Program processing

[1093] 1. Display and fill out the self-check sheet

[1094] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[1095] 2. Sending self-check data

[1096] The terminal transmits the self-check data entered by the user to the server.

[1097] 3. Analysis of self-check data

[1098] The server analyzes the received self-check data and identifies the user's skill level and concerns using natural language processing (NLP) technology.

[1099] 4. Selection of training materials and creation of training programs

[1100] Based on the analysis results, the server selects training materials that meet the user's needs from a training materials database and automatically generates a personalized training program. The selected training materials include videos, texts, and quizzes.

[1101] 5. Send and study the training program

[1102] The server again transmits the generated training program to the terminal, and the user starts learning on the terminal.

[1103] 6. Regular skill checks and training program updates

[1104] The server periodically sends skill check notifications to the terminal, and the user re-enters the self-check sheet according to the notifications. The system re-analyzes the new self-check data and updates the training program as necessary.

[1105] Specific examples

[1106] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[1107] 1. Self-check

[1108] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1109] 2. Data transmission and analysis

[1110] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[1111] 3. Selecting teaching materials and creating programs

[1112] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of training materials and automatically generates a training program that includes these.

[1113] 4. Send and study the training program

[1114] The server sends the generated training program to the terminal, and the user (Staff A) uses it to start learning.

[1115] 5. Regular skill checks and program updates

[1116] The server sends a skill check notification to the terminal one month later, and the user (Staff A) fills in the self-check sheet again. Based on this new data, the training program is updated as necessary.

[1117] In this way, the present invention is a system that can efficiently support users in improving their skills and eliminating their anxieties, thereby improving the quality of service and increasing staff retention rates.

[1118] The processing flow will be explained below.

[1119] Step 1:

[1120] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[1121] Step 2:

[1122] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[1123] Step 3:

[1124] The device collects the entered self-check data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1125] Step 4:

[1126] The server stores the received self-check data in a database, then uses natural language processing (NLP) to analyze the data and identify the user's skill level, concerns, and learning goals.

[1127] Step 5:

[1128] Based on the analysis results, the server selects appropriate training materials from a training materials database using a matching algorithm that takes into account the user's skill level and concerns.

[1129] Step 6:

[1130] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[1131] Step 7:

[1132] The server packages the generated training program and transmits it again to the terminal.

[1133] Step 8:

[1134] Users can access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[1135] Step 9:

[1136] The server notifies the user at regular intervals that a skill check will be conducted, and the user fills out the self-check sheet again.

[1137] Step 10:

[1138] The device collects new self-check data and sends it to the server, which then reanalyzes the data and updates the training program as the user progresses. This update involves selecting new learning materials and regenerating the program.

[1139] The above steps realize a system that continuously supports users in improving their skills and self-learning.

[1140] Example 1

[1141] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1142] Retail store staff need personalized training and instruction to improve their skills and resolve concerns about their work. However, traditional training methods have the problem of being unable to respond to individual needs and provide effective learning support. It is also difficult to grasp in a timely manner the level of skills staff possess and the concerns they have. For this reason, there is a need for an efficient training system that can improve staff motivation and increase staff retention rates.

[1143] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1144] In this invention, the server includes means for displaying a self-check sheet on an information processing device and receiving self-check data entered by a user, means for analyzing the received self-check data, and means for selecting appropriate educational materials from an educational material database based on the analysis results and automatically generating an educational program, thereby enabling efficient and personalized support for improving users' skills and resolving their anxieties.

[1145] An "information processing device" is a device for displaying the self-check sheet and accepting input from the user, and includes, for example, a tablet, PC, smartphone, etc.

[1146] The "self-check sheet" is an electronic form in which users can enter their skill level, concerns, and what they would like to learn.

[1147] "Self-check data" refers to information entered by the user into the self-check sheet, including skill level, areas of concern, and desired learning content.

[1148] The "server" is a computer system that receives and analyzes self-check data and generates and transmits educational programs.

[1149] "Analysis" refers to identifying the user's skill level and areas of concern based on the received self-check data, and includes the use of natural language processing technology.

[1150] An "educational materials database" is a database that stores educational materials in various formats, such as videos, documents, and tests.

[1151] An "educational program" is a training course that is automatically generated based on the analysis results to meet the user's needs, and includes selected educational materials.

[1152] "Packaging" means assembling the generated educational program in a form that is easy for users to use.

[1153] "Periodic" means repeated at regular intervals or on a schedule.

[1154] A "skill check" is a process that involves users completing and submitting a self-check sheet to reassess their skill level and any areas of concern.

[1155] "Natural language processing" is a technology that allows computers to understand and process human language, and is used to analyze self-check data.

[1156] The present invention is a system that supports independent learning by retail store staff with the aim of improving their own skills and relieving anxiety, and is implemented using a user device (terminal), a server, and a database.

[1157] System configuration

[1158] The system mainly consists of the following components:

[1159] 1. User Device (Terminal)

[1160] Example: Tablet, PC, Smartphone

[1161] Role: Displaying the self-check sheet and accepting user input

[1162] 2. Server

[1163] Role: Receiving data, analyzing, generating and sending educational programs

[1164] 3. Database

[1165] Example: Educational materials database

[1166] Role: Preservation and selection of educational materials

[1167] Program processing

[1168] Hardware and Software Use

[1169] User device (terminal): Displays the self-check sheet and sends the data entered by the user to the server. For example, a dedicated application is run on a tablet.

[1170] Server: Applying natural language processing (NLP) techniques to analyze the received data. Based on the analysis results, it selects appropriate educational materials from a database of educational materials and generates a personalized educational program using a generative AI model.

[1171] Database: Stores educational materials (videos, documents, tests, etc.) and provides the necessary materials in response to requests from the server.

[1172] Specific examples

[1173] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[1174] 1. Self-check

[1175] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1176] 2. Data transmission and analysis

[1177] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[1178] 3. Selecting teaching materials and creating programs

[1179] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of educational materials and automatically generates an educational program that includes these.

[1180] 4. Send and study the training program

[1181] The server sends the generated educational program to the terminal, and the user (Staff A) uses it to begin learning.

[1182] Prompt Sentence Examples

[1183] Use the following prompt for your generative AI model:

[1184] "Generate a training program based on the following information. For a user who has entered that they want to learn more about customer service skill deficiencies and cleaning procedures, suggest appropriate training materials and a training program based on that."

[1185] In this way, the present invention provides an efficient and personalized educational program that meets the needs of the user, thereby improving skills and eliminating concerns.

[1186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1187] Step 1: Display and fill out the self-check sheet

[1188] The server generates a self-check sheet at regular intervals or based on a specific event, and transmits it to the terminal.

[1189] Input: Trigger for self-check sheet generation by the server

[1190] Output: Send self-check sheet to terminal

[1191] The terminal displays the sent self-check sheet to the user.

[1192] Input: Self-check sheet sent from the server

[1193] Output: Self-check sheet displayed on the terminal screen

[1194] The user enters their skill level, concerns, and what they want to learn into the self-check sheet.

[1195] Input: User's skill level, concerns, and learning goals

[1196] Output: Data entry into self-check sheet

[1197] Specific actions: For example, enter "3 / 5" in the "Customer service skills" field, "Lack of product knowledge" in the "Concerns" field, and "Features of new products" in the "Things you want to learn" field.

[1198] Step 2: Submitting self-check data

[1199] The terminal transmits the self-check data entered by the user to the server.

[1200] Input: Self-check data entered by the user

[1201] Output: Self-check data sent to the server

[1202] Specific operation: When the input information is confirmed, press the data send button, and the following data will be sent to the server: "Customer service skills: 3 / 5", "Concerns: Lack of product knowledge", "Things to learn: Features of new products".

[1203] Step 3: Analyzing the self-check data

[1204] The server analyzes the received self-check data using natural language processing (NLP) technology.

[1205] Input: Received self-check data

[1206] Output: Identification of user skill level and concerns

[1207] Specific operation: For example, the NLP engine extracts keywords such as "customer service skills" and "product knowledge" and classifies them into their respective categories.

[1208] Step 4: Select training materials and create a training program

[1209] The server selects training materials that meet the user's needs from the training materials database based on the analysis results, and the selected training materials include videos, texts, and quizzes.

[1210] Input: Analysis results (user's skill level and concerns)

[1211] Output: List of selected training materials

[1212] Specific operation: For example, for a user who lacks customer service skills, "videos on customer service etiquette" and "basic responses" are selected.

[1213] The server generates a personalized training program based on the selected teaching materials.

[1214] Input: List of selected training materials

[1215] Output: Generated training program

[1216] Specific operations: For example, automatically generate a program that involves watching a video tutorial on customer service etiquette, then browsing a new product catalog, and finally checking comprehension with a simple quiz.

[1217] Step 5: Submit and study the training program

[1218] The server transmits the generated training program to the terminal.

[1219] Input: Generated training program

[1220] Output: Sends training program to terminal

[1221] The terminal notifies the user of the received training program and displays a learning screen.

[1222] Input: Training program sent from the server

[1223] Output: Notify the user and display the learning screen

[1224] The user follows the on-screen instructions and proceeds with their studies using the designated training materials.

[1225] Input: Training instructions from the terminal

[1226] Output: Learning progress

[1227] Specific operation: For example, the device screen displays "Please watch a video on customer service etiquette," and the user presses the play button to watch the video.

[1228] Step 6: Regularly check skills and update training programs

[1229] After the user has continued learning for a certain period of time, the server sends a notification to the terminal requesting a periodic skill check.

[1230] Input: A period of time has passed

[1231] Output: Skill check request notification to the device

[1232] The terminal displays the skill check sheet again to the user and prompts for input.

[1233] Input: Skill check request notification from the server

[1234] Output: Display of skill check sheet

[1235] The user then enters their new skill level and any concerns into the self-check sheet again.

[1236] Input: User's new skill level, anxiety

[1237] Output: Re-enter data into the self-check sheet

[1238] The server reanalyzes the data based on the new self-check and updates the training program as necessary.

[1239] Input: New self-check data

[1240] Output: Updated training program

[1241] Specific operation: For example, after one month, input "improvement status of customer service skills" and "new concerns." As a result, a training program incorporating additional materials and new quizzes will be provided in addition to the old program.

[1242] (Application example 1)

[1243] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1244] The problem that this invention aims to solve is to provide a system that allows store staff to study to efficiently improve their skills in between work. Conventional systems have made it difficult to provide personalized study programs tailored to each staff member's skill level and concerns, and to ensure that staff have time to study while on the job. As a result, staff skill improvement is limited, and the quality of service tends to decline.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1246] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data, means for selecting appropriate learning materials from a learning database based on the analysis results and automatically generating a learning program, means for packaging the generated learning program and transmitting it to the user device, means for periodically checking the user's skills and updating the learning program based on the self-check data, and means for being installed on a smartphone and allowing staff to efficiently study between work. This enables individual staff to efficiently carry out personalized learning according to their skill level and areas of concern, improving the quality of service and improving staff skills.

[1247] A "user device" is an electronic device used by a user, specifically a smartphone, tablet, PC, etc.

[1248] A "self-check sheet" is a questionnaire in which users enter their own skills and concerns.

[1249] "Self-check data" refers to the information entered by the user into the self-check sheet, and is data related to skill level and areas of concern.

[1250] "Server" refers to a computer system connected to a network that receives, analyzes, stores, and transmits data.

[1251] "Means for analysis" refers to the technology or algorithms used to analyze the received self-check data and understand its contents.

[1252] A "learning database" refers to a collection of data that stores training materials and information.

[1253] "Learning materials" are educational content provided to users, including videos, texts, quizzes, and the like.

[1254] "Learning Program" means an educational plan or course that combines selected learning materials to improve a User's skills.

[1255] "Packaging" refers to organizing the generated learning program into a single unit and making it in a form that can be delivered to a user device.

[1256] "Skill Check" refers to a periodic assessment method for evaluating a user's skill level.

[1257] "Means of being installed on a smartphone" refers to the placement and configuration of software so that the learning support system can run on a smartphone.

[1258] "Means for efficient learning between work tasks" refers to methods and techniques that allow users to efficiently carry out learning activities in between their regular work tasks.

[1259] The present invention is a system that links a user device (such as a smartphone or tablet), a server, and a learning database to enable store staff to efficiently improve their skills in between work.

[1260] System configuration

[1261] This system consists of the following components:

[1262] 1. User Device:

[1263] A device on which users fill out self-check sheets and receive and display learning programs. Specifically, this includes smartphones and tablets.

[1264] 2. Server:

[1265] It is the central system for receiving, analyzing, storing data, and generating and transmitting learning programs. The server has the following specific functions:

[1266] Regularly sending self-check sheets

[1267] Receiving and saving self-check data

[1268] Data analysis using natural language processing (NLP) techniques (e.g., Google Cloud Natural Language API)

[1269] Management of learning databases (e.g. MySQL)

[1270] Generate and send learning programs tailored to the user

[1271] 3. Learning database:

[1272] This is a database that stores educational materials (videos, texts, quizzes, etc.) to help users improve their skills.

[1273] Operation flow

[1274] Self-check and data transmission

[1275] The user (store staff) inputs the necessary information for the self-check sheet on the user device, such as "improving customer service skills" and "concerns about handling complaints." This data is sent from the user device to the server.

[1276] Data analysis and learning program generation

[1277] The server analyzes the received self-check data using natural language processing technology to identify the user's skill level and areas of concern, then selects appropriate learning materials from a learning database (e.g., video tutorials on customer service techniques, detailed manuals on handling complaints, etc.) and automatically generates a learning program optimized for the user.

[1278] Submitting and Viewing Learning Programs

[1279] The generated learning program is then sent back to the user device, and the user uses it to study. The user device can study to improve their skills between work tasks through the displayed learning program.

[1280] Skills check and program update

[1281] The server periodically sends a skill check notification to the user device, and the user fills in the self-check sheet again. Re-analysis is performed based on the new data, and the learning program is updated as necessary.

[1282] Specific examples

[1283] A specific example is Staff A at a certain store. Staff A uses his smartphone to input his "lack of customer service skills" and "concerns about handling complaints." The server analyzes this information using natural language processing technology, and generates a learning program from a learning database that includes a "video tutorial on customer service skills" and a "detailed manual on handling complaints," and sends it to Staff A's smartphone.

[1284] Prompt Sentence Examples

[1285] "What kind of materials should I provide to Staff A to help him improve his skills?"

[1286] This allows users to study efficiently while at work, improving their skills and alleviating anxiety.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] Display and enter information on the self-check sheet

[1290] The user device (smartphone) displays the self-check sheet sent from the server, and the user inputs information such as their skill level and concerns into the self-check sheet.

[1291] Input: Data entered by the user into the self-check sheet (e.g., lack of customer service skills, concerns about handling complaints)

[1292] Output: Self-check data sent from the user device

[1293] Step 2:

[1294] Sending self-check data

[1295] The user device sends the entered self-check data to the server, which transfers the data using a secure communication protocol (e.g., HTTPS).

[1296] Input: Self-check data entered by the user

[1297] Output: Self-check data sent to the server

[1298] Step 3:

[1299] Analysis of self-check data

[1300] The server analyzes the received self-check data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to analyze the text data and identify the user's skill level and areas of concern.

[1301] Input: Self-check data sent to the server

[1302] Output: Analyzed data (user skill level, identified concerns)

[1303] Step 4:

[1304] Selection of learning materials and creation of learning programs

[1305] Based on the analysis results, the server selects appropriate learning materials from the learning database and automatically generates a personalized learning program by extracting relevant videos, texts, and quizzes from the MySQL database.

[1306] Input: Parsed data

[1307] Output: The generated personalized learning program

[1308] Step 5:

[1309] Submitting and Viewing Learning Programs

[1310] The server transmits the generated learning program to the user device, which displays the program and allows the user to begin learning.

[1311] Input: Generated personalized learning program

[1312] Output: The learning program sent to the user device

[1313] Step 6:

[1314] Regular skill checks and learning program updates

[1315] The server periodically sends skill check notifications to the user device. The user re-enters the self-check sheet, and the server analyzes the new data and updates the learning program as necessary.

[1316] Input: Newly entered self-check data

[1317] Output: Updated learning program

[1318] Step 7:

[1319] Efficient learning in between work

[1320] Users can use their smartphones to study in between work hours, following the learning programs that have been sent to them. Specifically, they can use their free time or breaks to work on the learning materials.

[1321] Input: Learning activities according to the learning program

[1322] Output: Improved skill level and reduced anxiety

[1323] By following the specific steps, users can improve their skills efficiently and enhance the quality of their work.

[1324] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1325] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and by combining it with an emotion engine, provides effective training that also takes into account the emotional state of the user. The system is implemented using a user device, a server, a database, and an emotion engine.

[1326] System configuration

[1327] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc. The server receives and analyzes the self-check data and emotional data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[1328] Program processing

[1329] 1. Display and fill out the self-check sheet

[1330] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[1331] 2. Collecting Emotional Data

[1332] The device collects emotional data from the user's facial expressions, tone of voice, and text input. This is handled by the emotion engine.

[1333] 3. Sending self-check data and emotion data

[1334] The device sends the input self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[1335] 4. Data Analysis

[1336] The server stores the received self-check data and emotion data in a database, then analyzes the self-check data using natural language processing (NLP) and uses the emotion data to identify the user's skill level, anxiety factors, and emotional state.

[1337] 5. Selection of training materials and creation of training programs

[1338] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state using a matching algorithm that takes into account the user's skill level, anxiety factors, and emotional state.

[1339] 6. Send and study training programs

[1340] The server then packages the generated training program and sends it back to the device, where the user begins learning. They improve their skills by watching videos, reading texts, and answering quizzes.

[1341] 7. Regular skill checks and training program updates

[1342] The server periodically notifies the user when a skill check is due. The user then fills out the self-check sheet again, and their emotional data is updated. Based on the new self-check and emotional data, the training program is updated as needed. This update involves selecting new teaching materials and regenerating the program.

[1343] Specific examples

[1344] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, consider that Staff A is feeling stressed during training.

[1345] 1. Self-check

[1346] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1347] 2. Collecting Emotional Data

[1348] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice, collecting information that indicates he is feeling stressed.

[1349] 3. Data transmission and analysis

[1350] The device sends self-check data and emotion data to a server, and the server analyzes the received information using natural language processing technology and an emotion analysis engine.

[1351] 4. Teaching material selection and program creation

[1352] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[1353] 5. Send and study the training program

[1354] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[1355] 6. Regular skill checks and emotional data updates

[1356] The server notifies the user (Staff A) to periodically conduct skill checks and recollect emotional data, and the user fills out the self-check sheet again, updating the emotional data.

[1357] In this way, the present invention is a system that can improve users' skills, eliminate their anxieties, and provide efficient training that takes into account their emotional state, thereby improving the quality of service and increasing staff retention rates.

[1358] The processing flow will be explained below.

[1359] Step 1:

[1360] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[1361] Step 2:

[1362] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[1363] Step 3:

[1364] The device uses an emotion engine to collect emotional data from the user's facial expressions, tone of voice, and text input, including stress levels, motivation, and more.

[1365] Step 4:

[1366] The device sends the entered self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[1367] Step 5:

[1368] The server stores the received self-check data and emotion data in a database.

[1369] Step 6:

[1370] The server uses natural language processing (NLP) to analyze the self-check data and identify the user's skill level and anxiety factors. In parallel, it uses a sentiment analysis engine to analyze the user's emotional state (e.g., stress level, motivation).

[1371] Step 7:

[1372] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state. For example, for users with high stress levels, the server may consider adding videos on relaxation techniques.

[1373] Step 8:

[1374] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[1375] Step 9:

[1376] The server packages the generated training program and transmits it again to the terminal.

[1377] Step 10:

[1378] Users access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[1379] Step 11:

[1380] The server periodically notifies the user that a skill check will be conducted. The user then fills out the self-check sheet again and updates their emotional data.

[1381] Step 12:

[1382] The terminal collects new self-check data and emotion data and transmits them to the server.

[1383] Step 13:

[1384] The server analyzes the new data and updates the training program as needed, which includes selecting new materials and regenerating the program.

[1385] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, Staff A is experiencing stress during training.

[1386] 1. Self-check

[1387] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1388] 2. Collecting Emotional Data

[1389] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice and recognizes that Staff A is feeling stressed.

[1390] 3. Data transmission and analysis

[1391] The device sends self-check data and emotion data to a server, and the server analyzes the received information using natural language processing technology and an emotion analysis engine.

[1392] 4. Teaching material selection and program creation

[1393] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[1394] 5. Send and study the training program

[1395] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[1396] 6. Regular skill checks and emotional data updates

[1397] The server notifies the user (Staff A) to periodically conduct skill checks and recollect emotional data, and the user fills out the self-check sheet again, updating the emotional data.

[1398] This series of steps allows for more effective training programs to be provided based on detailed data, including the user's emotional state, via the emotion engine, helping to improve staff skills, alleviate anxiety, and even manage stress.

[1399] Example 2

[1400] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1401] While conventional training systems provide training programs based on the user's skill level and learning needs, they lack the ability to provide training programs that take into account the user's emotional state. This makes it difficult to provide effective training while reducing user stress and anxiety. Furthermore, the process for reflecting the results of periodic skill checks in the training program is insufficient, preventing continuous learning effects.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1403] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data and the user's emotional data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and recollecting their emotional data and updating the training program based on the self-check data and emotional data. This makes it possible to provide a personalized training program that takes into account the user's emotional state and corresponds to their skill level and anxiety factors.

[1404] "User device" refers to a terminal device used by a user, which has the function of displaying a self-check sheet and inputting data.

[1405] A "self-check sheet" is an input form that allows users to self-evaluate their skill level, learning needs, and areas of concern.

[1406] "Self-check data" refers to information that a user inputs into a self-check sheet via a user device.

[1407] "Emotional data" refers to data about a user's emotional state analyzed from facial expressions, tone of voice, text input, and the like.

[1408] "Server" refers to a central processing unit capable of analyzing data received from user devices and generating and transmitting appropriate training programs.

[1409] A "training materials database" refers to a database that stores training materials (videos, texts, quizzes, etc.) used in training.

[1410] A "training program" refers to a package of learning content that is automatically generated based on the user's skill level, concerns, and emotional state.

[1411] "Natural language processing (NLP)" refers to the technology that enables computers to understand and analyze human language.

[1412] A "matching algorithm" refers to a calculation method for selecting the most appropriate training materials based on user input data and analysis data.

[1413] "Encryption Technology" refers to the technology used to communicate data securely (e.g., HTTPS).

[1414] System Overview

[1415] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and provides effective training that takes into account the emotional state of the user by combining an emotion engine. This system is implemented using a user device, a server, a database, and an emotion engine.

[1416] Hardware and software used

[1417] User device (e.g., tablet, PC, smartphone): Displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc.

[1418] Server: Receives and analyzes the self-check data and emotion data sent from the user device. Furthermore, based on the analysis results, it has the function of selecting appropriate training materials from a training materials database and automatically generating a personalized training program.

[1419] Database: The training materials database stores a variety of training materials including videos, texts, and quizzes.

[1420] Emotion engine: Software for collecting and analyzing emotional data from users' facial expressions, tone of voice, and text input.

[1421] Hardware and software operation

[1422] 1. Display and fill out the self-check sheet

[1423] The server periodically sends a self-check sheet to the user device, which includes items for the user to fill in, such as their skill level, what they want to learn, and any concerns they may have.

[1424] The user enters his / her own condition into this self-check sheet.

[1425] 2. Collecting Emotional Data

[1426] The user device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, which are then analyzed by the emotion engine and output as numerical data.

[1427] 3. Sending self-check data and emotion data

[1428] The user device transmits the self-check data and emotion data entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[1429] 4. Data Analysis

[1430] The server stores the received self-check data and emotion data, analyzes the data using natural language processing (NLP), and identifies the user's skill level and emotional state based on the analysis results.

[1431] 5. Selection of training materials and creation of training programs

[1432] Based on the analysis results, the server selects training materials from the training materials database that match the user's needs and emotional state, using a matching algorithm.

[1433] The server automatically generates a personalized training program by combining the selected teaching materials.

[1434] 6. Send and study training programs

[1435] The server packages the generated training program and transmits it back to the user device.

[1436] Users can view training programs on their devices and progress through their studies, which include watching videos, reading texts, and answering quizzes.

[1437] 7. Regular skill checks and training program updates

[1438] The server periodically sends notifications to the user to check skills and re-collect emotion data.

[1439] The user fills in the self-check sheet again and updates the emotion data.

[1440] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[1441] Examples of concrete examples and prompt sentence usage

[1442] As a concrete example, consider a case where a retail store staff member wants to improve their customer service skills but feels they lack knowledge about cleaning procedures. They enter "poor customer service skills" and "I would like to learn more about cleaning procedures" into a self-check sheet on a terminal. The user's facial expressions and tone of voice indicate that they are feeling stressed. This data is sent to a server for analysis. Based on the analysis results, a training program is generated that includes a video to reinforce customer service skills, a detailed manual on cleaning procedures, and a video on stress reduction, and sent to the terminal.

[1443] An example prompt is:

[1444] "When retail store staff feel they lack customer service skills or are unsure about cleaning procedures, generate explanatory text that takes emotional data into account when generating training programs."

[1445] The above is an embodiment of the present invention.

[1446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1447] Step 1:

[1448] The server transmits a self-check sheet to the user device every Monday at 9:00 a.m. The sheet includes items for filling in skill level, areas of concern, and content that the user wants to learn.

[1449] Input: Recurring Schedule

[1450] Output: Self-check sheet displayed on the user's device

[1451] Step 2:

[1452] Users enter details of their own situation, such as "lack of customer service skills" or "want to learn cleaning procedures," into the self-check sheet displayed on the device.

[1453] Input: Self-check sheet

[1454] Output: Input self-check data

[1455] Step 3:

[1456] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time and transmits them to the emotion engine, which analyzes them and generates emotion data.

[1457] Input: User facial expressions, tone of voice, and text input

[1458] Output: Emotion data

[1459] Step 4:

[1460] The device transmits the self-check data entered by the user and the emotion data generated by the emotion engine to the server using a secure communication protocol (e.g., HTTPS).

[1461] Input: Self-check data and emotion data

[1462] Output: Data sent to the server

[1463] Step 5:

[1464] The server stores the received self-check data and emotion data, analyzes them using natural language processing (NLP), and identifies the user's skill level, anxiety factors, and emotional state based on the analysis results.

[1465] Input: Self-check data and emotion data

[1466] Output: Skill level, anxiety, and emotional state analysis results

[1467] Step 6:

[1468] Based on the analysis results, the server selects appropriate training materials from a database using a matching algorithm to extract materials that match the user's skill level, concerns, and emotional state.

[1469] Input: Analysis results

[1470] Output: Selected training materials

[1471] Step 7:

[1472] The server automatically generates a personalized training program by combining the selected learning materials, taking into account the user's skill level and emotional state.

[1473] Input: Selected training materials

[1474] Output: Personalized training program

[1475] Step 8:

[1476] The server then packages the generated training program and sends it back to the user's device. The user then views the training program on their device and progresses through the learning process, which includes watching videos, reading texts, and answering quizzes.

[1477] Input: Generated training program

[1478] Output: Packaged training program sent to user device

[1479] Step 9:

[1480] The server sends a notification to the user on the first day of each month to re-collect the skill check and emotional data. The user then fills out the self-check sheet again and updates the emotional data.

[1481] Input: Recurring Schedule

[1482] Output: Recollected self-check data and emotion data

[1483] Step 10:

[1484] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[1485] Input: Recollected self-check data and emotion data

[1486] Output: Updated training program

[1487] (Application example 2)

[1488] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1489] Traditional training systems often fail to provide sufficient support for staff skill development and anxiety relief. Furthermore, because they do not take into account the emotional state of staff, the training content may be inappropriate. Furthermore, regular skill checks are not conducted, which can delay long-term growth. This limits the effectiveness of training, making it difficult to improve staff retention and service quality.

[1490] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for displaying a self-check sheet on the user device and receiving self-check data entered by the user, means including an emotion engine for collecting emotion data from the user's facial expressions and tone of voice, means for analyzing the received and collected self-check data and emotion data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and updating the training program based on the self-check data and emotion data. This makes it possible to improve the user's skills and provide effective training that takes into account the user's emotional state.

[1491] definition statement

[1492] A "user device" is an electronic device that a user uses to fill out a self-check sheet or study a training program, and includes devices such as smartphones, tablets, and personal computers.

[1493] The "self-check sheet" is a questionnaire in which users can enter information about their skill level, areas of concern, and what they would like to learn.

[1494] "Self-check data" refers to data entered by the user into the self-check sheet, and includes the user's skill level, areas of concern, and desired learning content.

[1495] "Emotion data" refers to data relating to emotions collected from the user's facial expressions and tone of voice.

[1496] An "emotion engine" is software or hardware that analyzes a user's facial expressions and tone of voice to collect emotional data.

[1497] The "analysis means" has the function of analyzing the received and collected self-check data and emotion data, and uses techniques such as natural language processing.

[1498] The "Training Materials Database" is a database that stores various training materials, including video tutorials, text materials, quizzes, and the like.

[1499] A "training program" is an educational program automatically generated by combining training materials selected from a training material database based on the analysis results.

[1500] "Packaging" refers to assembling the generated training program into a format that can be sent to a user device.

[1501] A "skill check" is a check that a user performs periodically to reassess their skill level.

[1502] "Natural language processing" is a computer technology that analyzes text data entered by a user and extracts and classifies information based on that data.

[1503] MODE FOR CARRYING OUT THE INVENTION

[1504] The system implemented based on this invention is a support tool for retail store staff to learn independently, improve their skills, and alleviate their anxiety. The overall system configuration is as follows:

[1505] System configuration

[1506] The system includes a user device, a server, a database, and an emotion engine.

[1507] 1. User Device

[1508] The user device is used by the user to transmit the information entered in the self-check sheet and receive the training program, and typically includes a smartphone, tablet, or personal computer.

[1509] 2. Server

[1510] The server receives and analyzes the self-check data and emotion data sent from the user device, using natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[1511] 3. Database

[1512] The database stores training materials, which may be in various formats such as video tutorials, text materials, and quizzes.

[1513] 4. Emotion Engine

[1514] The emotion engine is designed to collect emotional data from the user's facial expressions and tone of voice. The system uses emotion recognition software such as DeepFace.

[1515] Program processing

[1516] The server first receives the self-check data sent from the user device, and then receives the emotion data collected through the camera and microphone installed on the user device. These data are transmitted using a secure communication protocol (e.g., HTTPS).

[1517] The received data is analyzed within the server. Natural language processing technology is used for the analysis to identify the user's skill level, concerns, and emotional state. For example, if a user enters "I would like to improve my customer service skills" into a self-check sheet and emotional data indicates a high stress level, appropriate training materials are selected based on this information.

[1518] The server then selects training materials from the training materials database that match the user's needs and emotional state, and automatically generates a personalized training program, which is then sent back to the user's device to enable the user to study.

[1519] Specific examples

[1520] Consider the example of a retail store staff member who wants to improve their customer service skills. The staff member uses their smartphone to fill out a self-checklist, writing, "I lack customer service skills," and "I would like to learn more about cleaning procedures." Next, the smartphone's camera and microphone are used to collect facial expressions and tone of voice, which are then analyzed by an emotion engine. The emotional data reveals that the staff member is feeling stressed during the training.

[1521] This data is then sent to a server, which analyzes it and generates training programs that include video tutorials on customer service techniques, detailed cleaning procedures, and stress reduction videos. Through this process, the training users receive becomes more effective and personalized.

[1522] Prompt Sentence Examples

[1523] "Enter your current skill level (ranging from 0-5). Next, indicate your current stress level: high, medium, or low. Finally, enter the topic you would like to learn about.

[1524] Example: Skill level: 4, Stress level: High, Topic to learn: Customer service etiquette.

[1525] This system allows users to receive the necessary training at the appropriate time, improving their skills and emotional state.

[1526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1527] Program processing steps

[1528] Step 1:

[1529] The user fills out a self-check sheet displayed on the user device (smartphone or tablet). Specifically, the user enters their skill level, areas of concern, and topics they would like to learn. The entered data is stored as self-check data.

[1530] input

[1531] Your skill level, stress level, and topics you want to learn

[1532] output

[1533] Self-check data

[1534] Data processing and calculation

[1535] Format the input data into a data structure such as JSON.

[1536] Step 2:

[1537] The device captures the user's facial expressions with a camera and collects the tone of voice with a microphone, and uses an emotion engine (e.g., DeepFace) to analyze the user's emotional state from these data.

[1538] input

[1539] Captured facial expression images and voice data

[1540] output

[1541] Emotional Data

[1542] Data processing and calculation

[1543] DeepFace and other emotion recognition software analyze images and audio to generate emotion data.

[1544] Step 3:

[1545] The device transmits the collected self-check data and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[1546] input

[1547] Self-check data, emotion data

[1548] output

[1549] Data sent to the server

[1550] Data processing and calculation

[1551] Data is encrypted and transmitted securely using HTTPS.

[1552] Step 4:

[1553] The server analyzes the received self-check data and emotion data, and uses natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[1554] input

[1555] Self-check data, emotion data

[1556] output

[1557] User skill level, fears, and emotional state

[1558] Data processing and calculation

[1559] Using natural language processing technology, text data is analyzed to identify skill levels and areas of concern.

[1560] Analyze the emotion data to determine the current emotional state.

[1561] Step 5:

[1562] Based on the analysis results, the server selects appropriate training materials from a training materials database and automatically generates a personalized training program.

[1563] input

[1564] User skill level, fears, and emotional state

[1565] Training materials database

[1566] output

[1567] Personalized Training Programs

[1568] Data processing and calculation

[1569] A matching algorithm is used to select the most appropriate training materials based on the analysis results.

[1570] A training program is created based on the selected teaching materials.

[1571] Step 6:

[1572] The server packages the generated training program and transmits it back to the user device.

[1573] input

[1574] Personalized Training Programs

[1575] output

[1576] Training program sent to user device

[1577] Data processing and calculation

[1578] The training program is packaged in an appropriate format so that it can be sent to the user's device.

[1579] Step 7:

[1580] Users run the training program on their device and study, improving their skills by watching videos, reading texts, and answering quizzes.

[1581] input

[1582] Training Program

[1583] output

[1584] Learning progress data

[1585] Data processing and calculation

[1586] Skill checks and emotional data are updated according to learning progress.

[1587] Step 8:

[1588] The server periodically notifies the user to take the skill check, collects new self-check data and emotion data from the user, and updates the training program based on the new data.

[1589] input

[1590] New self-check data and emotion data

[1591] output

[1592] Updated Training Program

[1593] Data processing and calculation

[1594] Compare and analyze new data with previous data and regenerate and update training programs as needed.

[1595] This process step allows users to receive ongoing appropriate training, improving their skills and emotional state.

[1596] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1597] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1598] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1599] [Fourth embodiment]

[1600] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1601] 7, a 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.

[1602] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1604] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1606] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1607] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1608] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1609] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1610] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1611] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1612] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] The present invention provides a system for supporting independent learning for retail store staff with the aim of improving their skills and alleviating anxiety. The system is implemented using a user device, a server, and a database.

[1614] System configuration

[1615] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. The server receives and analyzes the self-check data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[1616] Program processing

[1617] 1. Display and fill out the self-check sheet

[1618] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[1619] 2. Sending self-check data

[1620] The terminal transmits the self-check data entered by the user to the server.

[1621] 3. Analysis of self-check data

[1622] The server analyzes the received self-check data and identifies the user's skill level and concerns using natural language processing (NLP) technology.

[1623] 4. Selection of training materials and creation of training programs

[1624] Based on the analysis results, the server selects training materials that meet the user's needs from a training materials database and automatically generates a personalized training program. The selected training materials include videos, texts, and quizzes.

[1625] 5. Send and study the training program

[1626] The server again transmits the generated training program to the terminal, and the user starts learning on the terminal.

[1627] 6. Regular skill checks and training program updates

[1628] The server periodically sends skill check notifications to the terminal, and the user re-enters the self-check sheet according to the notifications. The system re-analyzes the new self-check data and updates the training program as necessary.

[1629] Specific examples

[1630] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[1631] 1. Self-check

[1632] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1633] 2. Data transmission and analysis

[1634] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[1635] 3. Selecting teaching materials and creating programs

[1636] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of training materials and automatically generates a training program that includes these.

[1637] 4. Send and study the training program

[1638] The server sends the generated training program to the terminal, and the user (Staff A) uses it to start learning.

[1639] 5. Regular skill checks and program updates

[1640] The server sends a skill check notification to the terminal one month later, and the user (Staff A) fills in the self-check sheet again. Based on this new data, the training program is updated as necessary.

[1641] In this way, the present invention is a system that can efficiently support users in improving their skills and eliminating their anxieties, thereby improving the quality of service and increasing staff retention rates.

[1642] The processing flow will be explained below.

[1643] Step 1:

[1644] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[1645] Step 2:

[1646] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[1647] Step 3:

[1648] The device collects the entered self-check data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1649] Step 4:

[1650] The server stores the received self-check data in a database, then uses natural language processing (NLP) to analyze the data and identify the user's skill level, concerns, and learning goals.

[1651] Step 5:

[1652] Based on the analysis results, the server selects appropriate training materials from a training materials database using a matching algorithm that takes into account the user's skill level and concerns.

[1653] Step 6:

[1654] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[1655] Step 7:

[1656] The server packages the generated training program and transmits it again to the terminal.

[1657] Step 8:

[1658] Users can access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[1659] Step 9:

[1660] The server notifies the user at regular intervals that a skill check will be conducted, and the user fills out the self-check sheet again.

[1661] Step 10:

[1662] The device collects new self-check data and sends it to the server, which then reanalyzes the data and updates the training program as the user progresses. This update involves selecting new learning materials and regenerating the program.

[1663] The above steps realize a system that continuously supports users in improving their skills and self-learning.

[1664] Example 1

[1665] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1666] Retail store staff need personalized training and instruction to improve their skills and resolve concerns about their work. However, traditional training methods have the problem of being unable to respond to individual needs and provide effective learning support. It is also difficult to grasp in a timely manner the level of skills staff possess and the concerns they have. For this reason, there is a need for an efficient training system that can improve staff motivation and increase staff retention rates.

[1667] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1668] In this invention, the server includes means for displaying a self-check sheet on an information processing device and receiving self-check data entered by a user, means for analyzing the received self-check data, and means for selecting appropriate educational materials from an educational material database based on the analysis results and automatically generating an educational program, thereby enabling efficient and personalized support for improving users' skills and resolving their anxieties.

[1669] An "information processing device" is a device for displaying the self-check sheet and accepting input from the user, and includes, for example, a tablet, PC, smartphone, etc.

[1670] The "self-check sheet" is an electronic form in which users can enter their skill level, concerns, and what they would like to learn.

[1671] "Self-check data" refers to information entered by the user into the self-check sheet, including skill level, areas of concern, and desired learning content.

[1672] The "server" is a computer system that receives and analyzes self-check data and generates and transmits educational programs.

[1673] "Analysis" refers to identifying the user's skill level and areas of concern based on the received self-check data, and includes the use of natural language processing technology.

[1674] An "educational materials database" is a database that stores educational materials in various formats, such as videos, documents, and tests.

[1675] An "educational program" is a training course that is automatically generated based on the analysis results to meet the user's needs, and includes selected educational materials.

[1676] "Packaging" means assembling the generated educational program in a form that is easy for users to use.

[1677] "Periodic" means repeated at regular intervals or on a schedule.

[1678] A "skill check" is a process that involves users completing and submitting a self-check sheet to reassess their skill level and any areas of concern.

[1679] "Natural language processing" is a technology that allows computers to understand and process human language, and is used to analyze self-check data.

[1680] The present invention is a system that supports independent learning by retail store staff with the aim of improving their own skills and relieving anxiety, and is implemented using a user device (terminal), a server, and a database.

[1681] System configuration

[1682] The system mainly consists of the following components:

[1683] 1. User Device (Terminal)

[1684] Example: Tablet, PC, Smartphone

[1685] Role: Displaying the self-check sheet and accepting user input

[1686] 2. Server

[1687] Role: Receiving data, analyzing, generating and sending educational programs

[1688] 3. Database

[1689] Example: Educational materials database

[1690] Role: Preservation and selection of educational materials

[1691] Program processing

[1692] Hardware and Software Use

[1693] User device (terminal): Displays the self-check sheet and sends the data entered by the user to the server. For example, a dedicated application is run on a tablet.

[1694] Server: Applying natural language processing (NLP) techniques to analyze the received data. Based on the analysis results, it selects appropriate educational materials from a database of educational materials and generates a personalized educational program using a generative AI model.

[1695] Database: Stores educational materials (videos, documents, tests, etc.) and provides the necessary materials in response to requests from the server.

[1696] Specific examples

[1697] A concrete example is Staff A at a retail store, who wants to improve his customer service skills and feels he lacks knowledge about cleaning procedures.

[1698] 1. Self-check

[1699] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1700] 2. Data transmission and analysis

[1701] The terminal sends this information to a server, which then analyzes it using natural language processing technology.

[1702] 3. Selecting teaching materials and creating programs

[1703] Based on the analysis results, the server selects "video tutorials on customer service techniques" and "detailed manuals on cleaning procedures" from a database of educational materials and automatically generates an educational program that includes these.

[1704] 4. Send and study the training program

[1705] The server sends the generated educational program to the terminal, and the user (Staff A) uses it to begin learning.

[1706] Prompt Sentence Examples

[1707] Use the following prompt for your generative AI model:

[1708] "Generate a training program based on the following information. For a user who has entered that they want to learn more about customer service skill deficiencies and cleaning procedures, suggest appropriate training materials and a training program based on that."

[1709] In this way, the present invention provides an efficient and personalized educational program that meets the needs of the user, thereby improving skills and eliminating concerns.

[1710] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1711] Step 1: Display and fill out the self-check sheet

[1712] The server generates a self-check sheet at regular intervals or based on a specific event, and transmits it to the terminal.

[1713] Input: Trigger for self-check sheet generation by the server

[1714] Output: Send self-check sheet to terminal

[1715] The terminal displays the sent self-check sheet to the user.

[1716] Input: Self-check sheet sent from the server

[1717] Output: Self-check sheet displayed on the terminal screen

[1718] The user enters their skill level, concerns, and what they want to learn into the self-check sheet.

[1719] Input: User's skill level, concerns, and learning goals

[1720] Output: Data entry into self-check sheet

[1721] Specific actions: For example, enter "3 / 5" in the "Customer service skills" field, "Lack of product knowledge" in the "Concerns" field, and "Features of new products" in the "Things you want to learn" field.

[1722] Step 2: Submitting self-check data

[1723] The terminal transmits the self-check data entered by the user to the server.

[1724] Input: Self-check data entered by the user

[1725] Output: Self-check data sent to the server

[1726] Specific operation: When the input information is confirmed, press the data send button, and the following data will be sent to the server: "Customer service skills: 3 / 5", "Concerns: Lack of product knowledge", "Things to learn: Features of new products".

[1727] Step 3: Analyzing the self-check data

[1728] The server analyzes the received self-check data using natural language processing (NLP) technology.

[1729] Input: Received self-check data

[1730] Output: Identification of user skill level and concerns

[1731] Specific operation: For example, the NLP engine extracts keywords such as "customer service skills" and "product knowledge" and classifies them into their respective categories.

[1732] Step 4: Select training materials and create a training program

[1733] The server selects training materials that meet the user's needs from the training materials database based on the analysis results, and the selected training materials include videos, texts, and quizzes.

[1734] Input: Analysis results (user's skill level and concerns)

[1735] Output: List of selected training materials

[1736] Specific operation: For example, for a user who lacks customer service skills, "videos on customer service etiquette" and "basic responses" are selected.

[1737] The server generates a personalized training program based on the selected teaching materials.

[1738] Input: List of selected training materials

[1739] Output: Generated training program

[1740] Specific operations: For example, automatically generate a program that involves watching a video tutorial on customer service etiquette, then browsing a new product catalog, and finally checking comprehension with a simple quiz.

[1741] Step 5: Submit and study the training program

[1742] The server transmits the generated training program to the terminal.

[1743] Input: Generated training program

[1744] Output: Sends training program to terminal

[1745] The terminal notifies the user of the received training program and displays a learning screen.

[1746] Input: Training program sent from the server

[1747] Output: Notify the user and display the learning screen

[1748] The user follows the on-screen instructions and proceeds with their studies using the designated training materials.

[1749] Input: Training instructions from the terminal

[1750] Output: Learning progress

[1751] Specific operation: For example, the device screen displays "Please watch a video on customer service etiquette," and the user presses the play button to watch the video.

[1752] Step 6: Regularly check skills and update training programs

[1753] After the user has continued learning for a certain period of time, the server sends a notification to the terminal requesting a periodic skill check.

[1754] Input: A period of time has passed

[1755] Output: Skill check request notification to the device

[1756] The terminal displays the skill check sheet again to the user and prompts for input.

[1757] Input: Skill check request notification from the server

[1758] Output: Display of skill check sheet

[1759] The user then enters their new skill level and any concerns into the self-check sheet again.

[1760] Input: User's new skill level, anxiety

[1761] Output: Re-enter data into the self-check sheet

[1762] The server reanalyzes the data based on the new self-check and updates the training program as necessary.

[1763] Input: New self-check data

[1764] Output: Updated training program

[1765] Specific operation: For example, after one month, input "improvement status of customer service skills" and "new concerns." As a result, a training program incorporating additional materials and new quizzes will be provided in addition to the old program.

[1766] (Application example 1)

[1767] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1768] The problem that this invention aims to solve is to provide a system that allows store staff to study to efficiently improve their skills in between work. Conventional systems have made it difficult to provide personalized study programs tailored to each staff member's skill level and concerns, and to ensure that staff have time to study while on the job. As a result, staff skill improvement is limited, and the quality of service tends to decline.

[1769] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1770] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data, means for selecting appropriate learning materials from a learning database based on the analysis results and automatically generating a learning program, means for packaging the generated learning program and transmitting it to the user device, means for periodically checking the user's skills and updating the learning program based on the self-check data, and means for being installed on a smartphone and allowing staff to efficiently study between work. This enables individual staff to efficiently carry out personalized learning according to their skill level and areas of concern, improving the quality of service and improving staff skills.

[1771] A "user device" is an electronic device used by a user, specifically a smartphone, tablet, PC, etc.

[1772] A "self-check sheet" is a questionnaire in which users enter their own skills and concerns.

[1773] "Self-check data" refers to the information entered by the user into the self-check sheet, and is data related to skill level and areas of concern.

[1774] "Server" refers to a computer system connected to a network that receives, analyzes, stores, and transmits data.

[1775] "Means for analysis" refers to the technology or algorithms used to analyze the received self-check data and understand its contents.

[1776] A "learning database" refers to a collection of data that stores training materials and information.

[1777] "Learning materials" are educational content provided to users, including videos, texts, quizzes, and the like.

[1778] "Learning Program" means an educational plan or course that combines selected learning materials to improve a User's skills.

[1779] "Packaging" refers to organizing the generated learning program into a single unit and making it in a form that can be delivered to a user device.

[1780] "Skill Check" refers to a periodic assessment method for evaluating a user's skill level.

[1781] "Means of being installed on a smartphone" refers to the placement and configuration of software so that the learning support system can run on a smartphone.

[1782] "Means for efficient learning between work tasks" refers to methods and techniques that allow users to efficiently carry out learning activities in between their regular work tasks.

[1783] The present invention is a system that links a user device (such as a smartphone or tablet), a server, and a learning database to enable store staff to efficiently improve their skills in between work.

[1784] System configuration

[1785] This system consists of the following components:

[1786] 1. User Device:

[1787] A device on which users fill out self-check sheets and receive and display learning programs. Specifically, this includes smartphones and tablets.

[1788] 2. Server:

[1789] It is the central system for receiving, analyzing, storing data, and generating and transmitting learning programs. The server has the following specific functions:

[1790] Regularly sending self-check sheets

[1791] Receiving and saving self-check data

[1792] Data analysis using natural language processing (NLP) techniques (e.g., Google Cloud Natural Language API)

[1793] Management of learning databases (e.g. MySQL)

[1794] Generate and send learning programs tailored to the user

[1795] 3. Learning database:

[1796] This is a database that stores educational materials (videos, texts, quizzes, etc.) to help users improve their skills.

[1797] Operation flow

[1798] Self-check and data transmission

[1799] The user (store staff) inputs the necessary information for the self-check sheet on the user device, such as "improving customer service skills" and "concerns about handling complaints." This data is sent from the user device to the server.

[1800] Data analysis and learning program generation

[1801] The server analyzes the received self-check data using natural language processing technology to identify the user's skill level and areas of concern, then selects appropriate learning materials from a learning database (e.g., video tutorials on customer service techniques, detailed manuals on handling complaints, etc.) and automatically generates a learning program optimized for the user.

[1802] Submitting and Viewing Learning Programs

[1803] The generated learning program is then sent back to the user device, and the user uses it to study. The user device can study to improve their skills between work tasks through the displayed learning program.

[1804] Skills check and program update

[1805] The server periodically sends a skill check notification to the user device, and the user fills in the self-check sheet again. Re-analysis is performed based on the new data, and the learning program is updated as necessary.

[1806] Specific examples

[1807] A specific example is Staff A at a certain store. Staff A uses his smartphone to input his "lack of customer service skills" and "concerns about handling complaints." The server analyzes this information using natural language processing technology, and generates a learning program from a learning database that includes a "video tutorial on customer service skills" and a "detailed manual on handling complaints," and sends it to Staff A's smartphone.

[1808] Prompt Sentence Examples

[1809] "What kind of materials should I provide to Staff A to help him improve his skills?"

[1810] This allows users to study efficiently while at work, improving their skills and alleviating anxiety.

[1811] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1812] Step 1:

[1813] Display and enter information on the self-check sheet

[1814] The user device (smartphone) displays the self-check sheet sent from the server, and the user inputs information such as their skill level and concerns into the self-check sheet.

[1815] Input: Data entered by the user into the self-check sheet (e.g., lack of customer service skills, concerns about handling complaints)

[1816] Output: Self-check data sent from the user device

[1817] Step 2:

[1818] Sending self-check data

[1819] The user device sends the entered self-check data to the server, which transfers the data using a secure communication protocol (e.g., HTTPS).

[1820] Input: Self-check data entered by the user

[1821] Output: Self-check data sent to the server

[1822] Step 3:

[1823] Analysis of self-check data

[1824] The server analyzes the received self-check data using natural language processing (NLP) technology. Specifically, it uses the Google Cloud Natural Language API to analyze the text data and identify the user's skill level and areas of concern.

[1825] Input: Self-check data sent to the server

[1826] Output: Analyzed data (user skill level, identified concerns)

[1827] Step 4:

[1828] Selection of learning materials and creation of learning programs

[1829] Based on the analysis results, the server selects appropriate learning materials from the learning database and automatically generates a personalized learning program by extracting relevant videos, texts, and quizzes from the MySQL database.

[1830] Input: Parsed data

[1831] Output: The generated personalized learning program

[1832] Step 5:

[1833] Submitting and Viewing Learning Programs

[1834] The server transmits the generated learning program to the user device, which displays the program and allows the user to begin learning.

[1835] Input: Generated personalized learning program

[1836] Output: The learning program sent to the user device

[1837] Step 6:

[1838] Regular skill checks and learning program updates

[1839] The server periodically sends skill check notifications to the user device. The user re-enters the self-check sheet, and the server analyzes the new data and updates the learning program as necessary.

[1840] Input: Newly entered self-check data

[1841] Output: Updated learning program

[1842] Step 7:

[1843] Efficient learning in between work

[1844] Users can use their smartphones to study in between work hours, following the learning programs that have been sent to them. Specifically, they can use their free time or breaks to work on the learning materials.

[1845] Input: Learning activities according to the learning program

[1846] Output: Improved skill level and reduced anxiety

[1847] By following the specific steps, users can improve their skills efficiently and enhance the quality of their work.

[1848] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1849] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and by combining it with an emotion engine, provides effective training that also takes into account the emotional state of the user. The system is implemented using a user device, a server, a database, and an emotion engine.

[1850] System configuration

[1851] The user device (e.g., tablet, PC, smartphone) displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc. The server receives and analyzes the self-check data and emotional data sent from the user device. Furthermore, the server has the function of selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a personalized training program. The generated program is then sent back to the user device, where the user studies.

[1852] Program processing

[1853] 1. Display and fill out the self-check sheet

[1854] The server periodically sends a self-check sheet to the terminal, and the user inputs their skill level, concerns, and what they want to learn.

[1855] 2. Collecting Emotional Data

[1856] The device collects emotional data from the user's facial expressions, tone of voice, and text input. This is handled by the emotion engine.

[1857] 3. Sending self-check data and emotion data

[1858] The device sends the input self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[1859] 4. Data Analysis

[1860] The server stores the received self-check data and emotion data in a database, then analyzes the self-check data using natural language processing (NLP) and uses the emotion data to identify the user's skill level, anxiety factors, and emotional state.

[1861] 5. Selection of training materials and creation of training programs

[1862] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state using a matching algorithm that takes into account the user's skill level, anxiety factors, and emotional state.

[1863] 6. Send and study training programs

[1864] The server then packages the generated training program and sends it back to the device, where the user begins learning. They improve their skills by watching videos, reading texts, and answering quizzes.

[1865] 7. Regular skill checks and training program updates

[1866] The server periodically notifies the user when a skill check is due. The user then fills out the self-check sheet again, and their emotional data is updated. Based on the new self-check and emotional data, the training program is updated as needed. This update involves selecting new teaching materials and regenerating the program.

[1867] Specific examples

[1868] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, consider that Staff A is feeling stressed during training.

[1869] 1. Self-check

[1870] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1871] 2. Collecting Emotional Data

[1872] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice, collecting information that indicates he is feeling stressed.

[1873] 3. Data transmission and analysis

[1874] The device sends self-check data and emotion data to a server, and the server analyzes the received information using natural language processing technology and an emotion analysis engine.

[1875] 4. Teaching material selection and program creation

[1876] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[1877] 5. Send and study the training program

[1878] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[1879] 6. Regular skill checks and emotional data updates

[1880] The server notifies the user (Staff A) to periodically conduct skill checks and recollect emotional data, and the user fills out the self-check sheet again, updating the emotional data.

[1881] In this way, the present invention is a system that can improve users' skills, eliminate their anxieties, and provide efficient training that takes into account their emotional state, thereby improving the quality of service and increasing staff retention rates.

[1882] The processing flow will be explained below.

[1883] Step 1:

[1884] The server periodically generates a self-check sheet and transmits it to the terminal, allowing the user to access the self-check sheet.

[1885] Step 2:

[1886] The terminal displays a self-check sheet, and the user inputs information about their skill level, concerns, and what they want to learn.

[1887] Step 3:

[1888] The device uses an emotion engine to collect emotional data from the user's facial expressions, tone of voice, and text input, including stress levels, motivation, and more.

[1889] Step 4:

[1890] The device sends the entered self-check data and collected emotion data to the server using a secure communication protocol (e.g., HTTPS).

[1891] Step 5:

[1892] The server stores the received self-check data and emotion data in a database.

[1893] Step 6:

[1894] The server uses natural language processing (NLP) to analyze the self-check data and identify the user's skill level and anxiety factors. In parallel, it uses a sentiment analysis engine to analyze the user's emotional state (e.g., stress level, motivation).

[1895] Step 7:

[1896] Based on the analysis results, the server selects training materials from a training materials database that match the user's needs and emotional state. For example, for users with high stress levels, the server may consider adding videos on relaxation techniques.

[1897] Step 8:

[1898] The server automatically generates a personalized training program based on the selected training materials, which includes videos, texts, and quizzes.

[1899] Step 9:

[1900] The server packages the generated training program and transmits it again to the terminal.

[1901] Step 10:

[1902] Users access the training program using their devices and begin learning, improving their skills by watching videos, reading texts, and answering quizzes.

[1903] Step 11:

[1904] The server periodically notifies the user that a skill check will be conducted. The user then fills out the self-check sheet again and updates their emotional data.

[1905] Step 12:

[1906] The terminal collects new self-check data and emotion data and transmits them to the server.

[1907] Step 13:

[1908] The server analyzes the new data and updates the training program as needed, which includes selecting new materials and regenerating the program.

[1909] As a concrete example, consider Staff A at a retail store. Staff A wants to improve their customer service skills and feels they lack knowledge about cleaning procedures. Furthermore, Staff A is experiencing stress during training.

[1910] 1. Self-check

[1911] The user (Staff A) enters "lack of customer service skills" and "I would like to learn more about cleaning procedures" into the self-check sheet on the device (tablet).

[1912] 2. Collecting Emotional Data

[1913] The device uses an emotion engine to analyze Staff A's facial expressions and tone of voice and recognizes that Staff A is feeling stressed.

[1914] 3. Data transmission and analysis

[1915] The device sends self-check data and emotion data to a server, and the server analyzes the received information using natural language processing technology and an emotion analysis engine.

[1916] 4. Teaching material selection and program creation

[1917] Based on the analysis results, the server selects from a database of training materials such as "video tutorials to reinforce customer service skills," "detailed manuals for cleaning procedures," and videos on stress reduction, and automatically generates a personalized training program incorporating these.

[1918] 5. Send and study the training program

[1919] The server sends the generated training program to the terminal, and the user (Staff A) uses the terminal to proceed with the study.

[1920] 6. Regular skill checks and emotional data updates

[1921] The server notifies the user (Staff A) to periodically conduct skill checks and recollect emotional data, and the user fills out the self-check sheet again, updating the emotional data.

[1922] This series of steps allows for more effective training programs to be provided based on detailed data, including the user's emotional state, via the emotion engine, helping to improve staff skills, alleviate anxiety, and even manage stress.

[1923] Example 2

[1924] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1925] While conventional training systems provide training programs based on the user's skill level and learning needs, they lack the ability to provide training programs that take into account the user's emotional state. This makes it difficult to provide effective training while reducing user stress and anxiety. Furthermore, the process for reflecting the results of periodic skill checks in the training program is insufficient, preventing continuous learning effects.

[1926] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1927] In this invention, the server includes means for displaying a self-check sheet on a user device and receiving self-check data entered by the user, means for analyzing the received self-check data and the user's emotional data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and recollecting their emotional data and updating the training program based on the self-check data and emotional data. This makes it possible to provide a personalized training program that takes into account the user's emotional state and corresponds to their skill level and anxiety factors.

[1928] "User device" refers to a terminal device used by a user, which has the function of displaying a self-check sheet and inputting data.

[1929] A "self-check sheet" is an input form that allows users to self-evaluate their skill level, learning needs, and areas of concern.

[1930] "Self-check data" refers to information that a user inputs into a self-check sheet via a user device.

[1931] "Emotional data" refers to data about a user's emotional state analyzed from facial expressions, tone of voice, text input, and the like.

[1932] "Server" refers to a central processing unit capable of analyzing data received from user devices and generating and transmitting appropriate training programs.

[1933] A "training materials database" refers to a database that stores training materials (videos, texts, quizzes, etc.) used in training.

[1934] A "training program" refers to a package of learning content that is automatically generated based on the user's skill level, concerns, and emotional state.

[1935] "Natural language processing (NLP)" refers to the technology that enables computers to understand and analyze human language.

[1936] A "matching algorithm" refers to a calculation method for selecting the most appropriate training materials based on user input data and analysis data.

[1937] "Encryption Technology" refers to the technology used to communicate data securely (e.g., HTTPS).

[1938] System Overview

[1939] The present invention is a system that supports independent learning for retail store staff with the aim of improving their skills and relieving anxiety, and provides effective training that takes into account the emotional state of the user by combining an emotion engine. This system is implemented using a user device, a server, a database, and an emotion engine.

[1940] Hardware and software used

[1941] User device (e.g., tablet, PC, smartphone): Displays the self-check sheet and accepts input from the user. It also has an emotion engine that collects emotional data from the user's facial expressions, tone of voice, text input, etc.

[1942] Server: Receives and analyzes the self-check data and emotion data sent from the user device. Furthermore, based on the analysis results, it has the function of selecting appropriate training materials from a training materials database and automatically generating a personalized training program.

[1943] Database: The training materials database stores a variety of training materials including videos, texts, and quizzes.

[1944] Emotion engine: Software for collecting and analyzing emotional data from users' facial expressions, tone of voice, and text input.

[1945] Hardware and software operation

[1946] 1. Display and fill out the self-check sheet

[1947] The server periodically sends a self-check sheet to the user device, which includes items for the user to fill in, such as their skill level, what they want to learn, and any concerns they may have.

[1948] The user enters his / her own condition into this self-check sheet.

[1949] 2. Collecting Emotional Data

[1950] The user device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, which are then analyzed by the emotion engine and output as numerical data.

[1951] 3. Sending self-check data and emotion data

[1952] The user device transmits the self-check data and emotion data entered by the user to the server using a secure communication protocol (e.g., HTTPS).

[1953] 4. Data Analysis

[1954] The server stores the received self-check data and emotion data, analyzes the data using natural language processing (NLP), and identifies the user's skill level and emotional state based on the analysis results.

[1955] 5. Selection of training materials and creation of training programs

[1956] Based on the analysis results, the server selects training materials from the training materials database that match the user's needs and emotional state, using a matching algorithm.

[1957] The server automatically generates a personalized training program by combining the selected teaching materials.

[1958] 6. Send and study training programs

[1959] The server packages the generated training program and transmits it back to the user device.

[1960] Users can view training programs on their devices and progress through their studies, which include watching videos, reading texts, and answering quizzes.

[1961] 7. Regular skill checks and training program updates

[1962] The server periodically sends notifications to the user to check skills and re-collect emotion data.

[1963] The user fills in the self-check sheet again and updates the emotion data.

[1964] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[1965] Examples of concrete examples and prompt sentence usage

[1966] As a concrete example, consider a case where a retail store staff member wants to improve their customer service skills but feels they lack knowledge about cleaning procedures. They enter "poor customer service skills" and "I would like to learn more about cleaning procedures" into a self-check sheet on a terminal. The user's facial expressions and tone of voice indicate that they are feeling stressed. This data is sent to a server for analysis. Based on the analysis results, a training program is generated that includes a video to reinforce customer service skills, a detailed manual on cleaning procedures, and a video on stress reduction, and sent to the terminal.

[1967] An example prompt is:

[1968] "When retail store staff feel they lack customer service skills or are unsure about cleaning procedures, generate explanatory text that takes emotional data into account when generating training programs."

[1969] The above is an embodiment of the present invention.

[1970] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1971] Step 1:

[1972] The server transmits a self-check sheet to the user device every Monday at 9:00 a.m. The sheet includes items for filling in skill level, areas of concern, and content that the user wants to learn.

[1973] Input: Recurring Schedule

[1974] Output: Self-check sheet displayed on the user's device

[1975] Step 2:

[1976] Users enter details of their own situation, such as "lack of customer service skills" or "want to learn cleaning procedures," into the self-check sheet displayed on the device.

[1977] Input: Self-check sheet

[1978] Output: Input self-check data

[1979] Step 3:

[1980] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time and transmits them to the emotion engine, which analyzes them and generates emotion data.

[1981] Input: User facial expressions, tone of voice, and text input

[1982] Output: Emotion data

[1983] Step 4:

[1984] The device transmits the self-check data entered by the user and the emotion data generated by the emotion engine to the server using a secure communication protocol (e.g., HTTPS).

[1985] Input: Self-check data and emotion data

[1986] Output: Data sent to the server

[1987] Step 5:

[1988] The server stores the received self-check data and emotion data, analyzes them using natural language processing (NLP), and identifies the user's skill level, anxiety factors, and emotional state based on the analysis results.

[1989] Input: Self-check data and emotion data

[1990] Output: Skill level, anxiety, and emotional state analysis results

[1991] Step 6:

[1992] Based on the analysis results, the server selects appropriate training materials from a database using a matching algorithm to extract materials that match the user's skill level, concerns, and emotional state.

[1993] Input: Analysis results

[1994] Output: Selected training materials

[1995] Step 7:

[1996] The server automatically generates a personalized training program by combining the selected learning materials, taking into account the user's skill level and emotional state.

[1997] Input: Selected training materials

[1998] Output: Personalized training program

[1999] Step 8:

[2000] The server then packages the generated training program and sends it back to the user's device. The user then views the training program on their device and progresses through the learning process, which includes watching videos, reading texts, and answering quizzes.

[2001] Input: Generated training program

[2002] Output: Packaged training program sent to user device

[2003] Step 9:

[2004] The server sends a notification to the user on the first day of each month to re-collect the skill check and emotional data. The user then fills out the self-check sheet again and updates the emotional data.

[2005] Input: Recurring Schedule

[2006] Output: Recollected self-check data and emotion data

[2007] Step 10:

[2008] The server updates the training program based on new self-check and emotion data, which includes selecting new learning materials and regenerating the program.

[2009] Input: Recollected self-check data and emotion data

[2010] Output: Updated training program

[2011] (Application example 2)

[2012] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2013] Traditional training systems often fail to provide sufficient support for staff skill development and anxiety relief. Furthermore, because they do not take into account the emotional state of staff, the training content may be inappropriate. Furthermore, regular skill checks are not conducted, which can delay long-term growth. This limits the effectiveness of training, making it difficult to improve staff retention and service quality.

[2014] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for displaying a self-check sheet on the user device and receiving self-check data entered by the user, means including an emotion engine for collecting emotion data from the user's facial expressions and tone of voice, means for analyzing the received and collected self-check data and emotion data, means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program, means for packaging the generated training program and transmitting it to the user device, and means for periodically checking the user's skills and updating the training program based on the self-check data and emotion data. This makes it possible to improve the user's skills and provide effective training that takes into account the user's emotional state.

[2015] definition statement

[2016] A "user device" is an electronic device that a user uses to fill out a self-check sheet or study a training program, and includes devices such as smartphones, tablets, and personal computers.

[2017] The "self-check sheet" is a questionnaire in which users can enter information about their skill level, areas of concern, and what they would like to learn.

[2018] "Self-check data" refers to data entered by the user into the self-check sheet, and includes the user's skill level, areas of concern, and desired learning content.

[2019] "Emotion data" refers to data relating to emotions collected from the user's facial expressions and tone of voice.

[2020] An "emotion engine" is software or hardware that analyzes a user's facial expressions and tone of voice to collect emotional data.

[2021] The "analysis means" has the function of analyzing the received and collected self-check data and emotion data, and uses techniques such as natural language processing.

[2022] The "Training Materials Database" is a database that stores various training materials, including video tutorials, text materials, quizzes, and the like.

[2023] A "training program" is an educational program automatically generated by combining training materials selected from a training material database based on the analysis results.

[2024] "Packaging" refers to assembling the generated training program into a format that can be sent to a user device.

[2025] A "skill check" is a check that a user performs periodically to reassess their skill level.

[2026] "Natural language processing" is a computer technology that analyzes text data entered by a user and extracts and classifies information based on that data.

[2027] MODE FOR CARRYING OUT THE INVENTION

[2028] The system implemented based on this invention is a support tool for retail store staff to learn independently, improve their skills, and alleviate their anxiety. The overall system configuration is as follows:

[2029] System configuration

[2030] The system includes a user device, a server, a database, and an emotion engine.

[2031] 1. User Device

[2032] The user device is used by the user to transmit the information entered in the self-check sheet and receive the training program, and typically includes a smartphone, tablet, or personal computer.

[2033] 2. Server

[2034] The server receives and analyzes the self-check data and emotion data sent from the user device, using natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[2035] 3. Database

[2036] The database stores training materials, which may be in various formats such as video tutorials, text materials, and quizzes.

[2037] 4. Emotion Engine

[2038] The emotion engine is designed to collect emotional data from the user's facial expressions and tone of voice. The system uses emotion recognition software such as DeepFace.

[2039] Program processing

[2040] The server first receives the self-check data sent from the user device, and then receives the emotion data collected through the camera and microphone installed on the user device. These data are transmitted using a secure communication protocol (e.g., HTTPS).

[2041] The received data is analyzed within the server. Natural language processing technology is used for the analysis to identify the user's skill level, concerns, and emotional state. For example, if a user enters "I would like to improve my customer service skills" into a self-check sheet and emotional data indicates a high stress level, appropriate training materials are selected based on this information.

[2042] The server then selects training materials from the training materials database that match the user's needs and emotional state, and automatically generates a personalized training program, which is then sent back to the user's device to enable the user to study.

[2043] Specific examples

[2044] Consider the example of a retail store staff member who wants to improve their customer service skills. The staff member uses their smartphone to fill out a self-checklist, writing, "I lack customer service skills," and "I would like to learn more about cleaning procedures." Next, the smartphone's camera and microphone are used to collect facial expressions and tone of voice, which are then analyzed by an emotion engine. The emotional data reveals that the staff member is feeling stressed during the training.

[2045] This data is then sent to a server, which analyzes it and generates training programs that include video tutorials on customer service techniques, detailed cleaning procedures, and stress reduction videos. Through this process, the training users receive becomes more effective and personalized.

[2046] Prompt Sentence Examples

[2047] "Enter your current skill level (ranging from 0-5). Next, indicate your current stress level: high, medium, or low. Finally, enter the topic you would like to learn about.

[2048] Example: Skill level: 4, Stress level: High, Topic to learn: Customer service etiquette.

[2049] This system allows users to receive the necessary training at the appropriate time, improving their skills and emotional state.

[2050] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2051] Program processing steps

[2052] Step 1:

[2053] The user fills out a self-check sheet displayed on the user device (smartphone or tablet). Specifically, the user enters their skill level, areas of concern, and topics they would like to learn. The entered data is stored as self-check data.

[2054] input

[2055] Your skill level, stress level, and topics you want to learn

[2056] output

[2057] Self-check data

[2058] Data processing and calculation

[2059] Format the input data into a data structure such as JSON.

[2060] Step 2:

[2061] The device captures the user's facial expressions with a camera and collects the tone of voice with a microphone, and uses an emotion engine (e.g., DeepFace) to analyze the user's emotional state from these data.

[2062] input

[2063] Captured facial expression images and voice data

[2064] output

[2065] Emotional Data

[2066] Data processing and calculation

[2067] DeepFace and other emotion recognition software analyze images and audio to generate emotion data.

[2068] Step 3:

[2069] The device transmits the collected self-check data and emotion data to a server using a secure communication protocol (e.g., HTTPS).

[2070] input

[2071] Self-check data, emotion data

[2072] output

[2073] Data sent to the server

[2074] Data processing and calculation

[2075] Data is encrypted and transmitted securely using HTTPS.

[2076] Step 4:

[2077] The server analyzes the received self-check data and emotion data, and uses natural language processing technology to identify the user's skill level, anxiety factors, and emotional state.

[2078] input

[2079] Self-check data, emotion data

[2080] output

[2081] User skill level, fears, and emotional state

[2082] Data processing and calculation

[2083] Using natural language processing technology, text data is analyzed to identify skill levels and areas of concern.

[2084] Analyze the emotion data to determine the current emotional state.

[2085] Step 5:

[2086] Based on the analysis results, the server selects appropriate training materials from a training materials database and automatically generates a personalized training program.

[2087] input

[2088] User skill level, fears, and emotional state

[2089] Training materials database

[2090] output

[2091] Personalized Training Programs

[2092] Data processing and calculation

[2093] A matching algorithm is used to select the most appropriate training materials based on the analysis results.

[2094] A training program is created based on the selected teaching materials.

[2095] Step 6:

[2096] The server packages the generated training program and transmits it back to the user device.

[2097] input

[2098] Personalized Training Programs

[2099] output

[2100] Training program sent to user device

[2101] Data processing and calculation

[2102] The training program is packaged in an appropriate format so that it can be sent to the user's device.

[2103] Step 7:

[2104] Users run the training program on their device and study, improving their skills by watching videos, reading texts, and answering quizzes.

[2105] input

[2106] Training Program

[2107] output

[2108] Learning progress data

[2109] Data processing and calculation

[2110] Skill checks and emotional data are updated according to learning progress.

[2111] Step 8:

[2112] The server periodically notifies the user to take the skill check, collects new self-check data and emotion data from the user, and updates the training program based on the new data.

[2113] input

[2114] New self-check data and emotion data

[2115] output

[2116] Updated Training Program

[2117] Data processing and calculation

[2118] Compare and analyze new data with previous data and regenerate and update training programs as needed.

[2119] This process step allows users to receive ongoing appropriate training, improving their skills and emotional state.

[2120] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2122] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2123] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2130] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2131] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2132] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2133] 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.

[2134] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2135] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2136] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2137] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2138] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2141] The following is further disclosed regarding the above embodiment.

[2142] That's true. Below are the draft patent claims for the distinctive parts of the system.

[2143] (Claim 1)

[2144] Displaying a self-check sheet on the user device;

[2145] means for receiving self-check data input by a user;

[2146] means for analyzing the received self-check data;

[2147] A means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program;

[2148] means for packaging and transmitting the generated training program to a user device;

[2149] a means for periodically checking the skills of users and updating the training program based on the self-check data;

[2150] A system including:

[2151] (Claim 2)

[2152] 10. The system of claim 1, further comprising: means for analyzing the user's skill level and anxiety factors from the received self-check data using natural language processing.

[2153] (Claim 3)

[2154] The system of claim 1 , wherein the training program includes training materials including videos, text, and quizzes.

[2155] "Example 1"

[2156] (Claim 1)

[2157] Display a self-check sheet on the information processing device,

[2158] means for receiving self-check data input by a user;

[2159] means for analyzing the received self-check data;

[2160] a means for selecting appropriate educational materials from an educational material database based on the analysis results and automatically generating an educational program;

[2161] means for packaging the generated educational program and transmitting the packaged educational program to an information processing device;

[2162] a means for periodically checking the skills of users and updating the training program based on the self-check data;

[2163] A system including:

[2164] (Claim 2)

[2165] 10. The system of claim 1, further comprising means for analyzing the user's skill level and anxiety factors from the received self-check data using natural language processing.

[2166] (Claim 3)

[2167] 10. The system of claim 1, wherein the educational program includes educational materials including videos, documents, and tests.

[2168] "Application Example 1"

[2169] (Claim 1)

[2170] Displaying a self-check sheet on the user device;

[2171] means for receiving self-check data input by a user;

[2172] means for analyzing the received self-check data;

[2173] A means for selecting appropriate learning materials from a learning database based on the analysis results and automatically generating a learning program;

[2174] means for packaging the generated learning program and transmitting it to the user device;

[2175] a means for periodically checking the user's skills and updating the learning program based on the self-check data;

[2176] It can be installed on smartphones and is a way for staff to study efficiently between work shifts.

[2177] A system including:

[2178] (Claim 2)

[2179] 10. The system of claim 1, further comprising: means for analyzing the user's skill level and anxiety factors from the received self-check data using natural language processing.

[2180] (Claim 3)

[2181] The system of claim 1 , wherein the learning program includes learning materials including videos, text, and quizzes.

[2182] "Example 2: Combining Emotion Engines"

[2183] (Claim 1)

[2184] Displaying a self-check sheet on the user device;

[2185] means for receiving self-check data input by a user;

[2186] means for analyzing the received self-check data and user emotion data;

[2187] A means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program;

[2188] means for packaging and transmitting the generated training program to a user device;

[2189] a means for periodically checking the skills of the user and recollecting emotion data, and updating the training program based on the self-check data and emotion data;

[2190] A system including:

[2191] (Claim 2)

[2192] 10. The system of claim 1, further comprising means for analyzing the user's skill level and emotional state from the received self-check data and emotion data using natural language processing.

[2193] (Claim 3)

[2194] The system of claim 1 , wherein the training program includes training materials including videos, text, and quizzes.

[2195] "Application example 2 when combining emotion engines"

[2196] (Claim 1)

[2197] Displaying a self-check sheet on the user device;

[2198] means for receiving self-check data input by a user;

[2199] means including an emotion engine for collecting emotion data from a user's facial expressions and tone of voice;

[2200] means for analyzing the received and collected self-check data and emotion data;

[2201] A means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program;

[2202] means for packaging and transmitting the generated training program to a user device;

[2203] a means for periodically checking the skills of the user and updating the training program based on the self-check data and emotion data;

[2204] A system including:

[2205] (Claim 2)

[2206] 10. The system of claim 1, further comprising means for analyzing the user's skill level, anxiety factors, and emotional state from the received self-check data and emotion data using natural language processing.

[2207] (Claim 3)

[2208] The system of claim 1 , wherein the training program includes training materials including videos, text, and quizzes. [Explanation of symbols]

[2209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. Displaying a self-check sheet on the user device; means for receiving self-check data input by a user; means for analyzing the received self-check data; A means for selecting appropriate training materials from a training materials database based on the analysis results and automatically generating a training program; means for packaging and transmitting the generated training program to a user device; a means for periodically checking the skills of users and updating the training program based on the self-check data; A system including:

2. The system of claim 1 , further comprising: means for analyzing the user's skill level and anxiety factors from the received self-check data using natural language processing.

3. The system of claim 1 , wherein the training program includes training materials including videos, text, and quizzes.

Images