System

Abstract

To provide a system which enables each employee to select a training for acquiring an optimal skill from among many training resources.SOLUTION: This system comprises: means for inputting information about user's job classification, carrier and characteristics; means for determining a skill necessary for the user on the basis of the input information; means for recommending a training on the basis of the determined skill; and means for generating job offer information using a generative AI model and presenting it to the user on the basis of a prompting message combined with the input information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Although there are many training resources within the company, it is difficult for individual employees to decide for themselves which training they should receive. The challenge is to select training that will enable employees to acquire the most appropriate skills based on their job type, career history, characteristics, etc. [Means for solving the problem]

[0005] We provide a system that inputs information such as the user's occupation, career history, and characteristics and determines the necessary skills based on that information. Based on the determined skills, we solve the above problem by recommending the most suitable training for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0014] [First embodiment]

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

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

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

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

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

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0027] "Example 1"

[0028] As one embodiment of the present invention, a system is provided that has an interface for users to input information such as their occupation, career history, characteristics, etc. This interface is designed to allow users to easily input information using text boxes, drop-down menus, etc.

[0029] "Example 2"

[0030] Next, a function is provided to determine the required skills based on the input information. This function uses machine learning algorithms and rule-based logic to determine the required skills from the user's information. For example, if the user is an engineer, it will determine that programming skills and project management skills are required.

[0031] "Example 3"

[0032] Finally, a function is provided that recommends training based on the skills determined. This function recommends training that is individually optimized for each user. For example, if it is determined that a user needs programming skills, it will recommend training in Python or Java (registered trademark). This allows users to efficiently acquire the skills they need.

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

[0034] "Example 1"

[0035] Step 1: The user enters information such as their job title, career history, and characteristics through the system interface. For example, the user enters information such as "Engineer," "5 years of experience," and "Java skills" into text boxes and drop-down menus.

[0036] "Example 2"

[0037] Step 1: The system takes the input and determines the required skills. This determination is made using machine learning algorithms and rule-based logic. For example, if a user inputs that they are an "engineer" and have "Java skills," the system might determine that they need skills such as "project management" and "database administration."

[0038] "Example 3"

[0039] Step 1: The system recommends training based on the skills identified. This recommendation is tailored to the individual user. For example, if the system determines that the skill "project management" is needed, it will recommend project management training.

[0040] Step 2: The user receives recommendations from the system and acquires the necessary skills by taking the recommended training. This allows the user to efficiently acquire the skills they need.

[0041] Example 1

[0042] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] In conventional systems, even if users input information such as their occupation, career history, and characteristics, it was difficult to effectively utilize that information to provide individually optimized career advice. In addition, there was a lack of a way to automatically determine the skills users needed and provide appropriate training and advice. This meant that users were unable to obtain specific guidance to effectively advance their career path.

[0044] 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.

[0045] In this invention, the server includes means for inputting information such as a user's occupation, career history, and characteristics, means for saving the input information in a database, means for processing and analyzing the saved information, means for generating prompt sentences based on the processed and analyzed information, means for sending the generated prompt sentences to a generative AI model, and means for providing the user with a response from the generative AI model, thereby enabling the user to receive individually optimized career advice based on their own information.

[0046] "User" refers to an individual who uses the system to input information such as their occupation, career history, and characteristics, and receives career advice.

[0047] "Occupation" refers to the type of work or duties that a user is engaged in.

[0048] "Career" refers to the job and work history a user has had.

[0049] "Features" refers to individual information such as a user's skills, expertise, and characteristics.

[0050] "Input means" refers to the interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[0051] "Database" refers to an information system for storing and managing information entered by users.

[0052] "Means for processing and analyzing" refers to the function of processing the information stored in the database and normalizing and filtering the data as necessary.

[0053] A "prompt sentence" refers to an instruction sentence for a generative AI model that is generated based on user input information.

[0054] A "generative AI model" refers to an artificial intelligence model that generates a response based on a prompt sentence based on user input information.

[0055] "Response" refers to the career advice or suggestions that the generative AI model generates based on the prompt.

[0056] "Means to provide" refers to the functionality for communicating responses from the generative AI model to the user.

[0057] This invention is a system that provides individually optimized career advice to users by inputting information such as their occupation, career history, and characteristics, and utilizing a generative AI model based on that information. Specific embodiments of this system are described below.

[0058] Hardware and software used

[0059] Hardware:

[0060] Terminal: The device through which a user enters information (e.g., PC, smartphone, tablet)

[0061] Server: A server for storing data, processing and analyzing it, and communicating with the generative AI model

[0062] software:

[0063] Database: A database system for storing user input information (e.g., MySQL (registered trademark), PostgreSQL)

[0064] Generative AI model: An artificial intelligence model that generates responses based on user input (e.g., GPT-4 (registered trademark))

[0065] Program processing

[0066] Users use their devices to access the system's interface and enter information such as their job title, career history, characteristics, etc. This interface includes UI components such as text boxes and drop-down menus, and is designed to allow users to easily enter information.

[0067] The terminal sends the information entered by the user to the server as an HTTP request, and the server stores the received information in a database and performs validation to maintain data integrity.

[0068] The server then processes and analyzes the stored information. Specifically, it standardizes the entered job titles and characteristics, and normalizes and filters the data as necessary. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0069] The server generates a prompt based on the processed and parsed information. For example, it generates the following prompt:

[0070] "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Could you please give me some career advice based on this information?"

[0071] The generated prompt is sent from the server to the generative AI model, which then generates an appropriate response based on the received prompt. For example, it might generate a response like, "Based on your experience and skill set, we recommend you move into cloud computing or data science as your next step."

[0072] Finally, the server sends the generated response to the device, where the user receives it, and receives specific career advice based on their job type and background.

[0073] Specific examples

[0074] For example, if a user enters the job title as "Software Engineer," experience of "5 years," and characteristics of "Python, Java," the following specific actions will occur:

[0075] 1. User enters "Software Engineer", "5 years", "Python, Java".

[0076] 2. The device sends this information to the server.

[0077] 3. The server stores the information in a database.

[0078] 4. The server normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0079] 5. The server generates the prompt: "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Based on this information, please give me some career advice that suits me."

[0080] 6. The server sends this prompt to the generative AI model.

[0081] 7. The generative AI model generates a response saying, "Based on your experience and skill set, we recommend your next steps be in cloud computing or data science."

[0082] 8. The server sends this response to the device.

[0083] 9. The user receives this response and gets advice on their next career step.

[0084] In this way, users can receive specific career advice based on their own information.

[0085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0086] Step 1:

[0087] The user accesses the interface.

[0088] Input: A user accesses the system's interface using a terminal.

[0089] Output: The interface is displayed, ready for the user to enter information.

[0090] What happens: The user launches a web browser or mobile app and opens a system URL or app.

[0091] Step 2:

[0092] The user inputs information such as occupation, career history, characteristics, etc.

[0093] Input: Users enter information such as job title, experience, and characteristics into text boxes and drop-down menus.

[0094] Output: The input information is temporarily stored on the device.

[0095] Specific operation: The user enters information such as "Software Engineer", "5 years", "Python, Java" into the input fields.

[0096] Step 3:

[0097] The terminal sends the input information to the server.

[0098] Input: Information entered by the user.

[0099] Output: The input information is sent to the server as an HTTP request.

[0100] Specific operation: The terminal generates an HTTP request including the information entered by the user and sends it to the server.

[0101] Step 4:

[0102] The server stores the received information in a database.

[0103] Input: User input information sent from the device.

[0104] Output: The information is saved in a database.

[0105] Specific operation: The server stores the received information in a database (e.g., MySQL, PostgreSQL) and performs validation to maintain data integrity.

[0106] Step 5:

[0107] The server processes and analyzes the stored information.

[0108] Input: User information stored in the database.

[0109] Output: Processed and analyzed information.

[0110] Specific operation: The server analyzes the stored information and standardizes job titles and characteristics. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0111] Step 6:

[0112] The server generates a prompt based on the processed and parsed information.

[0113] Input: Processed and analyzed user information.

[0114] Output: The generated prompt statement.

[0115] Specific behavior: Based on the user's information, the server generates a prompt statement: "I am a software engineer with 5 years of experience. I mainly use Python and Java. Based on this information, please provide me with suitable career advice."

[0116] Step 7:

[0117] The server sends the generated prompt to the generative AI model.

[0118] Input: The generated prompt statement.

[0119] Output: The prompt sent to the generative AI model.

[0120] Specific operation: The server sends the generated prompt sentence to a generative AI model (e.g., GPT-4).

[0121] Step 8:

[0122] A generative AI model generates a response.

[0123] Input: The prompt sent to the generative AI model.

[0124] Output: The generated response.

[0125] How it works: The generative AI model generates an appropriate response based on the prompt it receives, such as, "Based on your experience and skill set, I would recommend your next step be in cloud computing or data science."

[0126] Step 9:

[0127] The server sends the generated response to the terminal.

[0128] Input: The response from the generative AI model.

[0129] Output: The response sent to the terminal.

[0130] Specific operation: The server sends the response received from the generative AI model to the terminal.

[0131] Step 10:

[0132] The user receives the response.

[0133] Input: The response sent to the terminal.

[0134] Output: The response received by the user.

[0135] Specific operation: The user checks the response from the generative AI model through the device and receives advice on the next career step.

[0136] (Application example 1)

[0137] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] Conventional job information systems were unable to fully utilize information such as the user's job type, career history, and characteristics, making it difficult to provide users with the most suitable job information. Furthermore, they lacked the functionality to automatically determine the skills required by users and recommend appropriate training. This resulted in insufficient support for users to effectively advance their careers.

[0139] 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.

[0140] In this invention, the server includes means for inputting information such as the user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for generating optimal job information based on the input information, and means for providing job information using a generative AI model. This makes it possible to provide job information and training that are individually optimized for each user.

[0141] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc.

[0142] The "means for determining the required skills based on the input information" refers to a device or software that analyzes the information input by the user and automatically determines the skills the user needs based on that information.

[0143] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[0144] The "means for generating optimal job information based on input information" refers to a device or software that generates optimal job information for a user based on information input by the user.

[0145] A "means for providing job information using a generative AI model" is a device or software that uses a generative AI model to provide job information to users.

[0146] The system for implementing this invention provides an interface for users to input information such as their occupation, career history, characteristics, etc. The interface is implemented as a smartphone application and is designed to allow users to easily input information using text boxes and drop-down menus.

[0147] The server receives the information entered by the user and performs data analysis to determine the required skills. This analysis uses a Python program and the OpenAI (registered trademark) API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3 (registered trademark)) based on information such as the user's occupation, career history, and characteristics.

[0148] The server then recommends appropriate training to the user based on the determined skills, providing training programs optimized for the user's skill level and job type.

[0149] The server then generates optimal job listings based on the information entered by the user. Using a generative AI model, the server provides optimal job listings to the user. In this process, the server sends the user's job type, career history, characteristics, and other information to the generative AI model as prompts to generate optimal job listings.

[0150] As a concrete example, consider the case where a user enters the following information:

[0151] Job title: Software Engineer

[0152] Experience: 5 years of development experience

[0153] Specialties: Team leader experience

[0154] In this case, the prompt sent to the generative AI model would be:

[0155] Job title: Software Engineer

[0156] Experience: 5 years of development experience

[0157] Specialties: Team leader experience

[0158] Please provide the best job information based on this.

[0159] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[0160] This system is realized using a smartphone, a server, and a generative AI model (e.g., GPT-3). The smartphone provides the user interface, and the server analyzes data and calls the generative AI model. The generative AI model generates optimal job listings based on user input.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] A user uses a smartphone device to input information such as occupation, career history, and characteristics. The input interface includes text boxes and drop-down menus, and is designed to allow users to easily input information. The input information is then sent from the device to a server.

[0164] Input: Information such as user's occupation, career, characteristics, etc.

[0165] Output: User information sent to the server

[0166] Step 2:

[0167] The server analyzes the received user information and determines the required skills. This analysis is performed using a Python program and OpenAI API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3) based on information such as the user's occupation, career history, and characteristics.

[0168] Input: User information

[0169] Output: List of required skills

[0170] Step 3:

[0171] The server recommends appropriate training to the user based on the determined skills, the recommended training being optimized for the user's skill level and job type.

[0172] Input: List of required skills

[0173] Output: Recommended training program

[0174] Step 4:

[0175] The server generates optimal job information based on the information entered by the user. The generative AI model is used to provide optimal job information to the user. In this process, information such as the user's job type, career history, and characteristics is sent to the generative AI model as a prompt sentence, which generates optimal job information.

[0176] Input: User information

[0177] Output: Generated job listings

[0178] Step 5:

[0179] The server then sends the generated job information to the user's smartphone, allowing the user to check the most suitable job information on the smartphone.

[0180] Input: Generated job posting

[0181] Output: Job information displayed on the user's smartphone

[0182] As a specific example of operation, consider the case where the user inputs the following information:

[0183] Job title: Software Engineer

[0184] Experience: 5 years of development experience

[0185] Specialties: Team leader experience

[0186] In this case, the prompt sent to the generative AI model would be:

[0187] Job title: Software Engineer

[0188] Experience: 5 years of development experience

[0189] Specialties: Team leader experience

[0190] Please provide the best job information based on this.

[0191] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[0192] Example 2

[0193] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0194] Previous systems had the problem that it was difficult for users to identify the skills they needed based on their job type and career history, and there was a lack of information to receive appropriate training.In addition, there was a lack of means to recommend individually optimized training to users, which led to the issue of users not being able to improve their skills efficiently.

[0195] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for receiving and preprocessing the input information, a means for determining the required skills based on the preprocessed information, and a means for returning the determined skills to the user. This enables the user to automatically identify the required skills based on their occupation and career, and receive individually optimized training.

[0196] "User" means any individual or entity that uses the System.

[0197] "Occupation" is information that indicates the type of occupation the user is engaged in.

[0198] "Career" refers to historical information such as the user's past work experience and educational background.

[0199] "Characteristics" are a user's skills, characteristics, and other individual information.

[0200] The "means for inputting" is an interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[0201] "Means for receiving" refers to the function by which the server receives information sent by the user.

[0202] "Preprocessing" is the process of converting received information into a format that can be understood by a machine learning model.

[0203] The "means of determining" is a function that identifies the required skills based on preprocessed information.

[0204] "Means of return" is a function that notifies the user of the determined skill.

[0205] A "machine learning algorithm" is a computational method for learning patterns from data and making predictions and classifications.

[0206] "Rule-based logic" is a method of making decisions based on predefined rules.

[0207] "Training" is the process of educating and training users to acquire the necessary skills.

[0208] "Individually optimized training" means an education or training program that is customized to a user's specific needs and circumstances.

[0209] MODE FOR CARRYING OUT THE INVENTION

[0210] This invention is a system that allows users to identify the skills they need based on their job type and career history, and receive individually optimized training. This system is implemented using the following hardware and software.

[0211] Hardware and software used

[0212] Hardware: A server with a powerful CPU and sufficient memory

[0213] Software: Python, TENSORFLOW (registered trademark) (machine learning library), Scikit-learn (machine learning library), Flask (web framework)

[0214] System Overview

[0215] 1. Enter your user information:

[0216] The user uses the terminal to input information such as his / her occupation, career history, characteristics, etc.

[0217] Example: User types "Engineer."

[0218] 2. Data reception and preprocessing:

[0219] The server receives the information sent by the user.

[0220] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[0221] 3. Skill Assessment:

[0222] The server inputs the preprocessed data into the machine learning model.

[0223] It uses machine learning models (using TensorFlow and Scikit-learn) and rule-based logic to determine the skills a user needs.

[0224] Example: If the user types "engineer," the model determines that "programming skills" and "project management skills" are also required.

[0225] 4. Returning the results:

[0226] The server returns the determined skill to the user.

[0227] For example, it says, "You need programming and project management skills."

[0228] Examples and prompts

[0229] Examples:

[0230] If a user types in "data scientist," the system will determine that "data analysis skills" and "machine learning skills" are required.

[0231] If a user types in "marketing manager," the system will determine that "digital marketing skills" and "data analysis skills" are required.

[0232] Example prompt sentence:

[0233] "Please enter your occupation: e.g., Engineer, Data Scientist, Marketer"

[0234] "Please tell us your current skills: e.g., Python, project management, data analysis"

[0235] The system automatically determines the required skills by having users input information about their job type and skills, and provides them to users, allowing them to clearly understand the skills required for their career path.

[0236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0237] Step 1:

[0238] The user uses the terminal to enter information such as their occupation, career history, and characteristics.

[0239] Specifically, the user opens a browser on the device, accesses the system's web page, enters "engineer" in the occupation input field, and clicks the submit button.

[0240] Input: User's occupation, career, characteristics, etc.

[0241] Output: The information entered by the user is sent to the server

[0242] Step 2:

[0243] The server receives the information sent by the user.

[0244] Specifically, the server receives an HTTP request and obtains the user's input data.

[0245] Input: User-submitted information

[0246] Output: User information received by the server

[0247] Step 3:

[0248] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[0249] Specifically, the server tokenizes the text data and converts it into numeric data as needed.

[0250] Input: User information received by the server

[0251] Output: Preprocessed data

[0252] Step 4:

[0253] The server inputs the preprocessed data into the machine learning model.

[0254] Specifically, the server uses TensorFlow and Scikit-learn to input data into the model.

[0255] Input: Preprocessed data

[0256] Output: Skill assessment results from the machine learning model

[0257] Step 5:

[0258] The server uses machine learning models and rule-based logic to determine the skills a user needs.

[0259] Specifically, the model outputs "programming skills" and "project management skills" in response to the input "engineer."

[0260] Input: Preprocessed data

[0261] Output: List of required skills

[0262] Step 6:

[0263] The server returns the determined skill to the user.

[0264] Specifically, the server generates an HTTP response that tells the user, "You need programming skills and project management skills."

[0265] Input: List of required skills

[0266] Output: Skill information returned to the user

[0267] In this way, users can identify the skills they need based on their job type and background and receive individually optimized training.

[0268] (Application example 2)

[0269] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] While conventional systems can determine the required skills based on the user's job type and career history and recommend training, they have the problem of not being able to assign optimal tasks to factory workers. There was also a need for a system that could assign optimal tasks based on workers' years of experience, qualifications, and past performance evaluations.

[0271] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0272] In this invention, the server includes means for inputting information such as the user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information on workers in the factory and assigning optimal tasks. This makes it possible not only to recommend individually optimized training to the user, but also to assign optimal tasks to workers in the factory.

[0273] "User" refers to an individual or organization that uses the System.

[0274] "Job type" refers to the type of work or job a user is engaged in.

[0275] "Career" refers to a user's past work experience, educational background, qualifications, etc.

[0276] "Characteristics" refers to a user's individual characteristics, such as skills, abilities, and personality.

[0277] "Means for inputting information" refers to a method or device for inputting information such as a user's occupation, career, characteristics, etc. into the system.

[0278] "Means for determining required skills" refers to a method or device for identifying the skills required by a user based on input information.

[0279] "Training recommendation means" refers to a method or device for suggesting appropriate training to a user based on the determined skills.

[0280] "Factory workers" refers to employees working in a factory.

[0281] "Worker information" refers to data such as years of experience, qualifications, and past performance evaluations of workers in the factory.

[0282] The "means for assigning optimal work" refers to a method or device for determining and assigning the most suitable work to each worker based on information about the worker.

[0283] A system for implementing this invention includes means for inputting information such as a user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information about workers in a factory and assigning them the most appropriate work.

[0284] System configuration

[0285] The system consists of the following main components:

[0286] 1. User information input terminal: A terminal for inputting information such as the user's occupation, career, characteristics, etc. For example, a PC or tablet is used.

[0287] 2. Server: Processes the input information, determines the skills needed, recommends training, and processes information on workers in the factory to assign them the most appropriate tasks.

[0288] 3. Database: A database for storing user information, worker information, skill information, training information, etc.

[0289] 4. Machine learning algorithm: An algorithm to determine the required skills based on user information and assign optimal tasks. For example, RandomForestClassifier from the scikit-learn library is used.

[0290] Processing flow

[0291] 1. Input of user information: The user inputs information such as occupation, career, characteristics, etc. into the system through an input terminal.

[0292] 2. Information processing: The server stores the input information in a database and uses machine learning algorithms to determine the required skills.

[0293] 3. Training Recommendation: The server recommends the most appropriate training for the user based on the determined skills.

[0294] 4. Entering worker information: Enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals.

[0295] 5. Work allocation: The server allocates the optimal work based on the information of the workers.

[0296] Hardware and software used

[0297] Hardware: PCs, tablets, servers, database servers

[0298] Software: pandas library, scikit-learn library, database management system (e.g., MySQL)

[0299] Specific examples

[0300] For example, when a new worker joins a factory, the worker's years of experience, qualifications, and past performance evaluations are entered into the system via an input terminal. Based on this information, the server uses machine learning algorithms to assign the most suitable tasks.

[0301] Example prompt sentence:

[0302] Enter the new worker's information. Enter the number of years of experience, the number of certifications, and past performance ratings. For example, if the worker has 5 years of experience, 3 certifications, and a past performance rating of 4, enter the following:

[0303] Years of experience: 5

[0304] Number of qualifications: 3

[0305] Past performance rating: 4

[0306] In this way, the system can not only recommend individually optimized training for users, but also assign optimal tasks to workers within the factory.

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Step 1:

[0309] Users input information such as their occupation, career, and characteristics into the system through an input terminal. The input information is sent to the server and stored in a database. The input data includes the user's occupation, career, and characteristics.

[0310] Step 2:

[0311] The server retrieves user information stored in a database and uses a machine learning algorithm to determine the required skills. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the required skills based on the user's occupation and background. The input data is user information, and the output data is the required skills.

[0312] Step 3:

[0313] The server recommends the most suitable training for the user based on the determined skills. The recommended training information is sent to the user's device. The input data is the required skills, and the output data is the recommended training.

[0314] Step 4:

[0315] Users enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals. The entered information is sent to the server and stored in the database. The input data includes the worker's years of experience, qualifications, past performance evaluations, etc.

[0316] Step 5:

[0317] The server retrieves worker information stored in a database and uses a machine learning algorithm to assign the optimal task. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the optimal task based on the worker's years of experience, qualifications, and past performance evaluations. The input data is worker information, and the output data is the optimal task.

[0318] Step 6:

[0319] The server sends the assigned work information to the worker's terminal. The worker checks and executes the assigned work through the terminal. The input data is the optimal work, and the output data is the work information displayed on the worker's terminal.

[0320] Example 3

[0321] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] With conventional skill assessment systems, it was difficult to provide individually optimized training programs when determining the skills required based on the user's job type and career history. Furthermore, because user skill assessment was performed manually, there was a problem of reduced accuracy and efficiency. This made it difficult for users to efficiently acquire the skills they needed.

[0323] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0324] In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for transmitting the input information to the server, a means for the server to evaluate the user's skills using a generative AI model, a means for generating a training program based on the evaluation results, and a means for providing the generated training program to the user. This makes it possible to automatically evaluate the user's skills and provide an individually optimized training program.

[0325] "User" means an individual or organization that uses the System to receive a skill assessment or training program recommendation.

[0326] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[0327] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[0328] "Characteristics" refers to information that refers to a user's individual characteristics, such as skills, abilities, interests, and personality.

[0329] "Means for input" refers to the interface or device that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[0330] "Means for sending" refers to the communication means for sending the information entered by the user to the server.

[0331] "Server" refers to a computer system that receives and processes information sent by users.

[0332] A "generative AI model" refers to an artificial intelligence model that analyzes user information and performs skill evaluations and generates training programs.

[0333] "Means of evaluation" refers to the process or algorithm used to evaluate a user's skills using a generative AI model.

[0334] "Training Program" refers to the content of education and training that enables users to acquire the skills they need.

[0335] "Means for providing" refers to an interface or device for presenting the generated training program to the user.

[0336] This invention is a system that inputs information such as a user's occupation, career history, and characteristics, evaluates the required skills based on that information, and recommends the most appropriate training program. Specific embodiments of this system are described below.

[0337] First, the user enters information such as their occupation, career history, and characteristics into the device. The device then sends the entered information to the server. At this time, the device converts the information into JSON format and transmits it securely using the HTTPS protocol.

[0338] The server analyzes the received information and evaluates the user's skill using a generative AI model, such as OpenAI's GPT-4. The server inputs the following prompt into the generative AI model:

[0339] Assess users' skills and recommend the best training program for them. User input includes:

[0340] "I have basic programming skills, but I lack data science knowledge."

[0341] The generative AI model analyzes the prompt and evaluates the user's skills. Based on the evaluation results, the server generates the optimal training program for the user, for example, recommending a Python data analysis course to learn the basics of data science.

[0342] The server sends the generated training program to the terminal. The data is also encrypted when sent. The terminal displays the received training program to the user. The user can check the displayed training program and take it if necessary.

[0343] This system allows users to efficiently evaluate their skills and receive individually optimized training programs. This allows users to efficiently acquire the necessary skills. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0344] Step 1:

[0345] The user inputs information such as occupation, career history, characteristics, etc.

[0346] The user enters information such as their occupation, career history, and characteristics into the input form on the device. For example, they might enter, "I have basic programming skills, but I lack knowledge of data science." The entered information is temporarily stored in the device's memory.

[0347] Step 2:

[0348] The terminal transmits the input information to the server.

[0349] The terminal converts the information entered by the user into JSON format and sends it to the server using the HTTPS protocol. The input data is a JSON object containing information such as job type, career history, and characteristics. The transmitted data is received by the server's receiving module.

[0350] Step 3:

[0351] The server evaluates the user's skills using a generative AI model.

[0352] The server parses the received JSON data and inputs a prompt sentence to the generative AI model. The prompt sentence is generated as follows:

[0353] Assess users' skills and recommend the best training program for them. User input includes:

[0354] "I have basic programming skills, but I lack data science knowledge."

[0355] The generative AI model analyzes the prompt and evaluates the user's skills, outputting the results as data including the skills and level required by the user.

[0356] Step 4:

[0357] The server generates a training program based on the evaluation results.

[0358] The server searches the database for an appropriate training program based on the evaluation results obtained from the generative AI model. For example, it may select a Python data analysis course to learn the basics of data science. The generated training program is output as a JSON object containing content optimized for the user.

[0359] Step 5:

[0360] The server transmits the generated training program to the terminal.

[0361] The server converts the generated training program into JSON format and sends it to the device using the HTTPS protocol. The transmitted data is received by the device's receiving module.

[0362] Step 6:

[0363] The terminal displays the training program to the user.

[0364] The device parses the received JSON data and displays the contents of the training program to the user. The user can check the displayed training program and click the "Take Part" button if necessary. The displayed content includes details of the training and how to take part.

[0365] (Application example 3)

[0366] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] Conventional skill assessment systems can determine the skills required based on a user's job type and career history and recommend training, but they have the problem of not being able to assess skills in real time or recommend training based on that assessment. This makes it difficult for users to acquire the skills they actually need in a timely manner, and makes it difficult to improve their skills efficiently.

[0368] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results. This makes it possible to evaluate the user's skills in real time and recommend optimal training based on the evaluation.

[0369] "User" refers to any individual or entity that uses the System.

[0370] "Job type" refers to the type of work or role a user has.

[0371] "Career" refers to a user's past work experience, educational background, and other history.

[0372] "Traits" refer to the individual attributes of a user, such as their skills, abilities, interests, and personality.

[0373] "Means for inputting information" refers to an interface or device for inputting information such as the user's occupation, career, characteristics, etc. into the system.

[0374] "Means for determining required skills" refers to algorithms or programs that identify the skills a user needs based on input information.

[0375] "Training recommendation means" refers to a function that suggests appropriate training to users based on their assessed skills.

[0376] "Real-time assessment means" refers to sensors, data analysis algorithms, or programs that assess a user's skills in real time.

[0377] "Means for recommending training based on evaluation results" refers to a function for suggesting optimal training to users based on skill evaluation results obtained in real time.

[0378] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results.

[0379] System program generation

[0380] The server provides an interface for users to input information such as their occupation, career, and characteristics. Users input this information using smart glasses or a smartphone. The input information is sent to the server and stored in a database.

[0381] The server then runs an algorithm to determine the required skills based on the input information. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills.

[0382] Furthermore, the server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors in the smart glasses monitor the user's movements and send the data to the server, which then analyzes the data and evaluates the user's skill level.

[0383] Based on the evaluation results, the server will recommend optimal training to the user. For example, if the robot is not operating smoothly, the server will display a message on the smart glasses saying, "Robot operation training is recommended."

[0384] Hardware and software used

[0385] Hardware: smart glasses, smartphone, server

[0386] Software: Database management system, data analysis algorithms, user interface

[0387] Specific examples

[0388] Consider a scenario in which a factory robot operator is wearing smart glasses. When the operator starts operating the robot, the smart glasses monitor the operation and evaluate the operator's skills. For example, if the operator is not operating the robot smoothly, the smart glasses will display a message saying, "Robot operation training is recommended."

[0389] Prompt Sentence Examples

[0390] Develop an application that evaluates a user's skills and recommends necessary training. The user is a factory robot operator and wears smart glasses. Skill evaluation is performed in real time, and training recommendations are made based on the evaluation results. Specific skills include robot operation, maintenance, and programming. Training recommendations should be made for skills with an evaluation score of less than 5.

[0391] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0392] Step 1:

[0393] Users use smart glasses or smartphones to input information such as their occupation, career history, and characteristics. The input information is sent from the device to a server, which then stores the information in a database. The input data includes the user's occupation (e.g., factory robot operator), career history (e.g., 5 years of experience), and characteristics (e.g., programming skills).

[0394] Step 2:

[0395] The server runs an algorithm that determines the required skills based on the information stored in the database. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills. The input data is the user information, and the output data is a list of required skills.

[0396] Step 3:

[0397] The server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors installed in the smart glasses monitor the user's movements and send the data to the server. The server analyzes this data and evaluates the user's skill level. The input data is sensor data, and the output data is the skill evaluation result.

[0398] Step 4:

[0399] The server recommends optimal training to the user based on the evaluation results. For example, if the user's robot operation is not smooth, the server displays a message on the smart glasses saying, "We recommend training to operate the robot." The input data is the skill evaluation result, and the output data is a training recommendation message.

[0400] Step 5:

[0401] The user receives training recommendation messages from the server and starts the necessary training. The user reports the training progress to the server via smart glasses or a smartphone. The server stores the training progress in a database and monitors the improvement of the user's skill level. The input data is the training progress, and the output data is the updated skill evaluation results.

[0402] 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.

[0403] "Example 1"

[0404] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[0405] "Example 2"

[0406] The emotion engine also determines the skills required based on the user's emotions. For example, if the emotion engine recognizes that the user is feeling angry, it determines that communication skills and problem-solving skills are required. This makes it possible to acquire skills according to the user's emotional state.

[0407] "Example 3"

[0408] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[0409] The processing flow of each embodiment will be described below.

[0410] "Example 1"

[0411] Step 1: The emotion engine recognizes the user's emotion.

[0412] Step 2: Adjust training recommendations based on the emotions recognized by the emotion engine.

[0413] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will recommend relaxation and stress management training.

[0414] Step 4: If the emotion engine also detects that the user is experiencing joy, it will recommend training to maintain that joy.

[0415] "Example 2"

[0416] Step 1: The emotion engine recognizes the user's emotion.

[0417] Step 2: Determine the skills required based on the emotions recognized by the emotion engine.

[0418] Step 3: For example, if the emotion engine detects that the user is angry, it determines that communication skills and problem-solving skills are needed.

[0419] Example 1

[0420] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0421] Conventional training recommendation systems can determine the necessary skills based on the user's job type and career history and recommend training, but they do not take into account the user's emotional state when recommending training. As a result, they are unable to provide optimal training according to the user's emotional state, which can lead to reduced user satisfaction and effectiveness.

[0422] 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.

[0423] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for receiving the input information and storing it in a database, means for analyzing the user's emotions based on the stored information, and means for recommending training based on the analyzed emotions, thereby making it possible to recommend optimal training according to the user's emotional state.

[0424] "User" refers to an individual or organization who uses the system to input information such as their occupation, career history, and characteristics, and receives training recommendations.

[0425] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[0426] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[0427] "Characteristics" refers to information that refers to a user's personal characteristics, skills, interests, etc.

[0428] "Input means" refers to an interface that allows a user to provide information such as their occupation, career, characteristics, etc. to the system.

[0429] "Database" refers to a storage device or system for organizing and storing information entered by users.

[0430] "Means of emotion analysis" refers to technologies and algorithms used to recognize and analyze emotions from user input.

[0431] "Training recommendation means" refers to a function for suggesting suitable training to a user based on the analyzed emotions.

[0432] "Natural language processing technology" refers to technology for analyzing meaning and emotions from text data entered by users.

[0433] MODE FOR CARRYING OUT THE INVENTION

[0434] This invention is a system that allows a user to input information such as their occupation, career, and characteristics, analyzes emotions based on that information, and recommends optimal training. Specific embodiments of this system are described below.

[0435] 1. Program Generation

[0436] The server provides an interface for the user to enter information. This interface includes text boxes and drop-down menus designed to make it easy for the user to enter information.

[0437] 2. Program Processing

[0438] The server receives the information entered by the user and stores it in a database, which organizes and stores information such as the user's occupation, career, and characteristics.

[0439] The server then uses an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing (NLP) technology to analyze emotions from the user's input. Specifically, it uses "natural language processing technology" to extract emotions from the user's text.

[0440] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user enters "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend relaxation and stress management training.

[0441] 3. Specific Examples

[0442] As a specific example, consider the case where a user inputs "I've been busy at work lately and I'm feeling stressed."

[0443] 1. Using the device interface, the user types "I've been busy and stressed out lately" into a text box.

[0444] 2. The server receives this information and stores it in a database as "Occupation: Office Worker, Emotion: Stress."

[0445] 3. The server uses an emotion engine (e.g., natural language processing technology) to analyze the emotion "stress."

[0446] 4. Based on the analysis results, the server recommends "relaxation and stress management training."

[0447] 5. Users undergo recommended training and practice specific methods to reduce stress (e.g., deep breathing, meditation).

[0448] Prompt Sentence Examples

[0449] If a user types, "I've been busy and stressed lately at work," how would the emotion engine respond and what training would it recommend?

[0450] In this way, an optimal workout is provided depending on the user's emotional state.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] The user enters information

[0454] The user uses the terminal interface to input information such as their occupation, career, and characteristics. The input information is sent to the server through text boxes and drop-down menus. The input data includes text information such as occupation, career, and characteristics.

[0455] Step 2:

[0456] The server receives the information and stores it in a database

[0457] The server receives the information sent by the user and stores it in a database. Specifically, it analyzes the received text data and stores it in the appropriate fields. For example, it may be stored in a format such as "Occupation: Company employee," "Career: 5 years of experience," or "Characteristics: Low stress tolerance." The input data is text information, and the output data is structured data stored in the database.

[0458] Step 3:

[0459] The server uses an emotion engine to analyze the user's emotions.

[0460] The server starts an emotion engine based on the stored information. The emotion engine uses natural language processing technology to analyze emotions from the user's input. For example, natural language processing technology can be used to extract emotions such as "stress" or "joy" from the user's text. The input data is text information, and the output data is analyzed emotional information.

[0461] Step 4:

[0462] The server recommends training based on the analysis results.

[0463] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user inputs "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend training related to relaxation and stress management. The input data is the analyzed emotion information, and the output data is the recommended training content.

[0464] Step 5:

[0465] Users receive recommended training

[0466] The user receives training recommendations from the server. The training content is optimized according to the user's emotional state, providing specific methods for reducing stress and maintaining joy. For example, relaxation techniques such as deep breathing and meditation are recommended. The input data is the recommended training content, and the output data is the user's practice results.

[0467] (Application example 1)

[0468] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0469] While conventional systems can determine the necessary skills based on a user's job type and career history and recommend training, they have the problem of not being able to adjust training or workload taking into account the user's emotional state, making it difficult to provide optimal training and work environments that take into account the user's stress and emotional state.

[0470] 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.

[0471] In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state. This makes it possible to provide optimal training and a working environment according to the user's emotional state.

[0472] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc. into the system.

[0473] "Means for determining required skills based on input information" refers to a device or software that analyzes information such as job type, career history, and characteristics input by the user and automatically determines the skills the user requires.

[0474] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[0475] "Means for recognizing user emotions" refers to a device or software that analyzes the user's facial expressions, voice, actions, etc., and recognizes the user's emotional state.

[0476] A "means for adjusting training recommendations based on recognized emotions" is a device or software that adjusts the content or method of training based on the recognized emotional state of the user.

[0477] The "means for adjusting the workload according to the user's emotional state" refers to a device or software that adjusts the pace or workload of work according to the user's emotional state.

[0478] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state.

[0479] Generating a Program

[0480] The program of this system is configured as follows:

[0481] 1. Enter your user information:

[0482] The server provides an interface for users to enter information about their job title, background, characteristics, etc. This may include text boxes and drop-down menus.

[0483] 2. Determine the skills needed:

[0484] The server analyzes the input information and automatically determines the skills the user needs, using machine learning algorithms and database searches.

[0485] 3. Training Recommendations:

[0486] Based on the skills determined, the server recommends appropriate training to the user, which is optimized for the user's job type and background.

[0487] 4. Emotion Recognition:

[0488] The server analyzes the user's facial expressions, voice, and behavior to recognize the user's emotional state. This analysis is performed using an emotion recognition engine (EmotionEngine).

[0489] 5. Training Adjustments:

[0490] The server then adjusts the content and method of training based on the user's perceived emotional state, for example recommending relaxation training if the user is feeling stressed.

[0491] 6. Workload adjustment:

[0492] The server adjusts the pace and workload of the work according to the user's emotional state, for example, slowing down the work pace if the user is feeling stressed.

[0493] Hardware and software used

[0494] Hardware: Factory robots, emotion-recognizing cameras, speakers

[0495] Software: EmotionEngine (emotion recognition engine), Robot (robot control library)

[0496] Specific examples

[0497] For example, if the emotion engine detects that a factory worker is feeling stressed from working long hours, the server can instruct the robot to slow down its work pace and play relaxation music, reducing the operator's stress and improving work efficiency.

[0498] Prompt Sentence Examples

[0499] Examples of prompts for a generative AI model might include:

[0500] If the user is feeling stressed, the robot should generate a program to slow down its work pace and play relaxation music.

[0501] In this way, it is possible to provide an optimal training or working environment according to the user's emotional state.

[0502] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0503] Step 1:

[0504] The server provides an interface for users to enter information such as their job title, background, characteristics, etc. Users enter information using text boxes and drop-down menus. The entered information is sent to the server and stored in a database.

[0505] Input: User's occupation, career, characteristics, etc.

[0506] Output: User information stored in the database

[0507] Step 2:

[0508] The server analyzes user information stored in a database and automatically determines the skills the user needs, using machine learning algorithms and database searches.

[0509] Input: User information stored in the database

[0510] Output: A list of skills the user needs

[0511] Step 3:

[0512] Based on the skills determined, the server recommends appropriate training to the user, which is optimized for the user's job type and background.

[0513] Input: A list of skills the user needs

[0514] Output: A list of recommended workouts

[0515] Step 4:

[0516] The server analyzes the user's facial expressions, voice, and behavior to recognize the user's emotional state. This analysis is performed using an emotion recognition engine (EmotionEngine).

[0517] Input: User facial, voice, and behavioral data

[0518] Output: User's emotional state

[0519] Step 5:

[0520] The server then adjusts the content and method of training based on the user's perceived emotional state, for example recommending relaxation training if the user is feeling stressed.

[0521] Input: User's emotional state, list of recommended workouts

[0522] Output: List of adjusted trainings

[0523] Step 6:

[0524] The server adjusts the pace and workload of the work according to the user's emotional state, for example, slowing down the work pace if the user is feeling stressed.

[0525] Input: User's emotional state

[0526] Output: Adjusted work pace or load

[0527] In this way, it is possible to provide an optimal training or working environment according to the user's emotional state.

[0528] Example 2

[0529] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0530] While conventional skill assessment systems can determine the skills required based on a user's occupation and career history, they are unable to consider the user's emotional state. As a result, they are unable to determine appropriate skills or recommend training based on the user's emotional state, which means they are unable to fully meet the user's needs.

[0531] 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.

[0532] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for determining required skills based on the input information, means for analyzing the user's emotional state, means for determining required skills based on the emotional state, and means for recommending training based on the determined skills. This makes it possible to determine skills and recommend training that take into account not only the user's occupation and career, but also their emotional state.

[0533] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to the interface that allows the user to input information such as their occupation, past experience, skills, and characteristics into the system.

[0534] "Means for determining required skills based on input information" refers to algorithms and logic that analyze information input by the user, such as job type and career history, and identify the skills the user needs based on that information.

[0535] "Means for analyzing the user's emotional state" refers to technologies or engines for recognizing emotions from text or voice data entered by the user and analyzing that emotional state.

[0536] "Means for determining required skills based on emotional state" refers to algorithms or logic for identifying the skills a user needs based on the analyzed emotional state of the user.

[0537] "Training recommendations based on determined skills" refers to a system or functionality that suggests appropriate training or learning resources to users based on the identified skills needs.

[0538] This invention is a system that determines the necessary skills based on information input by the user, and further analyzes the emotional state of the user to recommend appropriate skills. Specific embodiments for implementing this system are described below.

[0539] Hardware and software used

[0540] Hardware: Servers, terminals (PCs, smartphones)

[0541] Software: Machine learning libraries (TensorFlow, scikit-learn), emotion recognition engines (e.g., IBM Watson® Tone Analyzer)

[0542] System Overview

[0543] 1. Enter your user information

[0544] A user uses a terminal to input his / her own information (occupation, skills, emotional state, etc.). For example, a user inputs information such as "engineer" or "angry" into an input form on the terminal.

[0545] 2. Transmission of information

[0546] The terminal transmits the input information to the server. Specifically, the terminal transmits the user input information to the server using an HTTP request.

[0547] 3. Analysis of Information

[0548] The server analyzes the information it receives and uses machine learning algorithms and rule-based logic to determine the skills needed based on the user's occupation and skills. For example, the server might use TensorFlow and scikit-learn to determine that a user's occupation as an "engineer" requires programming and project management skills.

[0549] 4. Emotion Analysis

[0550] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes emotions from the text and voice data entered by the user. For example, the server uses IBM Watson Tone Analyzer to recognize the user's emotion of "anger" and determines that communication skills and problem-solving skills are required.

[0551] 5. Providing results

[0552] The server sends the analysis results to the device. The device then displays a list of required skills to the user. Specifically, the server uses an HTTP response to send the analysis results to the device, and the device displays "Required skills: programming skills, project management skills, communication skills, problem-solving skills" to the user.

[0553] Examples of concrete examples and prompts

[0554] Specific examples

[0555] If the user is an engineer and inputs that their current emotional state is "anger," the system determines that programming skills, project management skills, communication skills, and problem-solving skills are required. The user enters "engineer" and "anger" into the input form on the device and clicks the send button. The device sends the information to the server, which analyzes it and returns the results to the device. The device displays the results to the user.

[0556] Prompt Sentence Examples

[0557] "I'm an engineer and I'm currently experiencing anger. What skills do I need?"

[0558] This system allows users to efficiently acquire skills that correspond to their occupation and emotional state.

[0559] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0560] Step 1:

[0561] Entering user information

[0562] The user uses the terminal to input his / her own information (occupation, skills, emotional state, etc.).

[0563] Input: The user enters information such as "engineer" or "anger" into the device's input form.

[0564] Specific actions: The user uses the device's keyboard or touchscreen to enter the required information into the input form and clicks the submit button.

[0565] Step 2:

[0566] Sending information

[0567] The terminal transmits the input information to the server.

[0568] Input: User-entered information (occupation, skills, emotional state)

[0569] Output: User information sent to the server

[0570] Specific operation: The device uses an HTTP request to send the user's input information to the server.

[0571] Step 3:

[0572] Analysis of information

[0573] The server analyzes the information it receives and uses machine learning algorithms and rule-based logic to determine the skills needed based on the user's occupation and skills.

[0574] Input: User information received by the server (occupation, skills)

[0575] Output: List of required skills

[0576] What it does: The server uses TensorFlow and scikit-learn to determine that the user's occupation is "engineer" and that they need programming and project management skills.

[0577] Step 4:

[0578] Emotion Analysis

[0579] The server analyzes the user's emotional state using an emotion engine, which recognizes emotions from the text and voice data entered by the user.

[0580] Input: User emotion information received by the server

[0581] Output: List of required skills based on emotional state

[0582] Specific operation: The server uses IBM Watson Tone Analyzer to recognize the user's emotion of "anger" and determines that communication skills and problem-solving skills are required.

[0583] Step 5:

[0584] Providing results

[0585] The server sends the analysis results to the device, which then displays a list of required skills to the user.

[0586] Input: List of required skills parsed by the server

[0587] Output: A list of required skills displayed on the device

[0588] Specific operation: The server sends the analysis results to the device using an HTTP response. The device displays the user's "Required skills: programming skills, project management skills, communication skills, problem-solving skills."

[0589] (Application example 2)

[0590] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0591] While conventional skill assessment systems can determine the necessary skills based on a user's job type and career history, they are unable to take the user's emotional state into account, making it difficult to provide appropriate skills in response to the user's emotions. Robots working in factories are also required to sense the emotional state of workers and provide appropriate skills, but conventional systems were unable to achieve this.

[0592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0593] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for determining required skills based on the input information, means for sensing the user's emotional state, means for determining required skills based on the sensed emotional state, and means for recommending training based on the determined skills. This makes it possible to provide appropriate skills that take into account not only the user's occupation and career but also their emotional state.

[0594] "User" refers to an individual or organization that uses the System.

[0595] "Job type" refers to the type of work or role a user has.

[0596] "Career" refers to the user's history, including the jobs and educational background they have had.

[0597] "Traits" refer to individual attributes such as a user's skills, personality, interests, etc.

[0598] "Means for inputting information" refers to a method or device for inputting information such as a user's occupation, career, characteristics, etc. into the system.

[0599] "Means for determining required skills" refers to a method or device for identifying the skills a user needs based on input information.

[0600] "Means for sensing emotional state" refers to a method or device for detecting a user's emotions.

[0601] "Means for determining required skills based on a sensed emotional state" refers to a method or apparatus for identifying required skills based on a sensed emotional state of a user.

[0602] "Training recommendation means" refers to a method or device for suggesting appropriate training to a user based on the determined skills.

[0603] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining required skills based on the input information, means for sensing the user's emotional state, means for determining required skills based on the sensed emotional state, and means for recommending training based on the determined skills.

[0604] System program generation

[0605] The system executes the following programs. First, it provides a user interface as a means for inputting information such as the user's occupation, career, and characteristics. This interface includes forms and options for the user to enter their information. Next, it uses machine learning algorithms and rule-based logic to determine the required skills based on the input information. Furthermore, it performs emotion recognition using hardware such as a camera and microphone to sense the user's emotional state. It uses an emotion engine to determine the required skills based on the sensed emotional state. Finally, it provides an individually optimized training program to recommend training based on the determined skills.

[0606] Explanation of program processing

[0607] The server provides a user interface for entering information such as the user's job title, background, and characteristics. This interface can be accessed through a web browser or a mobile application. Once the user enters the information, the server stores the information in a database and uses machine learning algorithms (e.g., RandomForestClassifier) ​​to determine the required skills.

[0608] Next, the device uses a camera and microphone to sense the user's emotional state. An emotion recognition library (e.g., EmotionRecognizer) is used to analyze the user's emotional state in real time. Based on the sensed emotional state, the server uses an emotion engine to reassess the required skills and identify appropriate skills.

[0609] Finally, based on the skills determined, the server recommends an individually optimized training program for the user, customized according to the user's job type, background, and emotional state.

[0610] Specific examples

[0611] For example, if this system is installed on a robot working in a factory, the robot will input the worker's job type and career information to determine the required skills. Furthermore, the robot will use a camera and microphone to sense the worker's emotional state and reassess the required skills using an emotion engine. Finally, the robot will provide each worker with an individually optimized training program.

[0612] Prompt Sentence Examples

[0613] User information: "Engineer, 5 years experience"

[0614] Emotion data: "Anger 0.2, Happiness 0.8"

[0615] Using this prompt as input, the system determines the skills required and recommends an appropriate training program.

[0616] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0617] Step 1:

[0618] The user inputs information such as occupation, career history, characteristics, etc.

[0619] Input: Information such as user's occupation, career, characteristics, etc.

[0620] Output: User information entered

[0621] Specific operation: A user enters information such as their occupation, career, and characteristics into a form provided through a web browser or mobile application. The entered information is sent to the server.

[0622] Step 2:

[0623] The server stores the entered information in a database.

[0624] Input: User information entered

[0625] Output: User information stored in the database

[0626] Specific operation: The server stores the received user information in a database, which allows it to retain the information needed for subsequent processing.

[0627] Step 3:

[0628] The server uses machine learning algorithms to determine the required skills.

[0629] Input: User information stored in the database

[0630] Output: Determined required skills

[0631] Specific operation: The server retrieves user information from the database and uses a machine learning algorithm (e.g., RandomForestClassifier) ​​to determine the skills required by the user. The determined skills are temporarily stored.

[0632] Step 4:

[0633] The device senses the user's emotional state.

[0634] Input: Real-time video and audio data of the user

[0635] Output: Sensed emotional state data

[0636] Specific operation: The device uses a camera and microphone to capture real-time video and audio of the user, and analyzes the user's emotional state using an emotion recognition library (e.g., EmotionRecognizer). The detected emotional state data is sent to the server.

[0637] Step 5:

[0638] The server reassess the required skills based on the sensed emotional state.

[0639] Input: Sensed emotional state data, determined required skills

[0640] Output: Reassessed required skills

[0641] Specific operation: The server receives the sensed emotional state data and re-evaluates the required skills using the emotion engine, thereby identifying the appropriate skills according to the user's emotional state.

[0642] Step 6:

[0643] The server recommends training based on the determined skills.

[0644] Input: Reassessed required skills

[0645] Output: Recommended training program

[0646] Specific operation: The server generates and recommends an individually optimized training program for the user based on the reassessed required skills. The recommended training program is notified to the user.

[0647] Example 3

[0648] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0649] Conventional training recommendation systems can determine the necessary skills based on the user's occupation and career history and recommend training, but they have the problem of not being able to adjust training based on the user's emotional state. This makes it difficult to provide optimal training when the user is feeling stressed or in a particular emotional state.

[0650] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for inputting information such as the user's occupation, career history, and characteristics, a means for determining the required skills based on the input information, a means for recommending training based on the determined skills, a means for recognizing the user's emotional state, and a means for adjusting the content of the training based on the recognized emotional state. This makes it possible to provide optimal training that takes into account both the user's skills and emotional state.

[0651] "User" refers to any individual or entity that uses the System.

[0652] "Job type" refers to the type of work or job a user is engaged in.

[0653] "Career" refers to a user's past work experience, educational background, qualifications, etc.

[0654] "Traits" refer to individual attributes such as a user's skills, interests, personality, etc.

[0655] "Means for inputting information" refers to an interface for inputting information such as the user's occupation, career, characteristics, etc. into the system.

[0656] "Means for determining required skills" refers to algorithms or programs that identify the skills a user needs based on input information.

[0657] "Training recommendation means" refers to a function that suggests appropriate training to users based on their assessed skills.

[0658] "Means for recognizing emotional states" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[0659] "Means for adjusting training content" refers to a function for changing the difficulty or content of training based on the recognized emotional state.

[0660] This invention is a system that inputs information such as a user's occupation, career history, and characteristics, determines the required skills based on that information, and further adjusts the training content by recognizing the user's emotional state. A specific embodiment of this system will be described below.

[0661] 1. Collecting user skill data

[0662] The device collects information such as the user's occupation, career history, and characteristics. This information can be obtained from the self-evaluation sheet the user fills out and past training history. For example, the device collects the user's history of taking "Basic Programming Course" and "Intermediate Data Analysis Course."

[0663] 2. Skills data analysis

[0664] The server analyzes the collected skill data. Specifically, it uses Python data analysis libraries (e.g., pandas, scikit-learn) to organize the data and evaluate the user's skill level. For example, it can confirm that a user has taken the "Basic Programming Course" but has not yet taken the "Intermediate Data Analysis Course."

[0665] 3. Training Recommendations

[0666] The server recommends the most suitable training for the user based on the analysis results. The server identifies the user's skill gaps and lists training courses to fill those gaps. For example, if the user has not taken the "Intermediate Data Analysis Course," the server recommends that the user take the "Intermediate Data Analysis Course."

[0667] 4. Collecting user sentiment data

[0668] The device collects facial and voice data from the user. Specifically, it uses a webcam and microphone to capture the user's facial expressions and voice in real time, for example, collecting facial expressions and vocalizations made by the user during training.

[0669] 5. Emotional Data Analysis

[0670] The server analyzes the collected emotional data, specifically using OpenCV and TensorFlow to analyze the user's facial and voice data to identify their emotional state, for example, whether they are feeling stressed or happy.

[0671] 6. Adjust your training based on emotions

[0672] The server adjusts the training content based on the analysis results of the emotion engine. Specifically, it changes the difficulty and content of the training depending on the user's emotional state. For example, if the user is feeling stressed, it will recommend relaxation and stress management training.

[0673] Examples and prompts

[0674] Skills recommended:

[0675] Example: If it is determined that User A needs programming skills, the server will recommend training courses such as "Basic Programming Course" and "Intermediate Data Analysis Course."

[0676] Emotion-based recommendations:

[0677] Example: If User B is identified as feeling stressed, the server may recommend relaxation and stress management training.

[0678] Prompt Sentence Examples

[0679] Skills recommended:

[0680] "Recommend optimal programming training based on the user's past activity history and self-assessment data."

[0681] Emotion-based recommendations:

[0682] "Analyze the user's facial and voice data and recommend relaxation training if they are feeling stressed."

[0683] In this way, the system can provide optimal training based on the user's skills and emotional state. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0684] Step 1: Collect user skill data

[0685] The terminal collects information such as the user's occupation, career, and characteristics.

[0686] Input: Self-evaluation sheets and past training history entered by users.

[0687] Specific operation: The terminal retrieves the self-evaluation sheet filled out by the user and the history of past training sessions from the database.

[0688] Output: Collected information about the user's occupation, background, characteristics, etc.

[0689] Step 2: Analyze your skills data

[0690] The server analyzes the collected skill data.

[0691] Input: Collected information about the user, such as their occupation, background, and characteristics.

[0692] How it works: The server organizes the data using Python's pandas library and uses scikit-learn to evaluate the user's skill level using a machine learning model.

[0693] Output: Analysis of the user's skill level.

[0694] Step 3: Training Recommendations

[0695] The server then recommends the most suitable training for the user based on the analysis results.

[0696] Input: Analysis results about the user's skill level.

[0697] What it does: The server identifies the user's skill gaps and lists training courses to fill those gaps.

[0698] Output: A list of recommended training courses.

[0699] Step 4: Collect user sentiment data

[0700] The device collects the user's facial expressions and voice data.

[0701] Input: User's facial expressions and voice.

[0702] What it does: The device uses a webcam and microphone to capture the user's facial expressions and voice in real time.

[0703] Output: Collected facial and voice data of the user.

[0704] Step 5: Analyze the sentiment data

[0705] The server analyzes the collected emotion data.

[0706] Input: Collected facial and voice data of the user.

[0707] How it works: The server uses OpenCV and TensorFlow to analyze the user's facial expression and voice data to identify their emotional state.

[0708] Output: Analysis results about the user's emotional state.

[0709] Step 6: Adjust your training based on emotions

[0710] The server adjusts the training content based on the analysis results of the emotion engine.

[0711] Input: Analysis results about the user's emotional state.

[0712] Specific operation: The server changes the difficulty and content of the training depending on the user's emotional state.

[0713] Output: Adjusted training content.

[0714] In this way, the system can provide optimal training based on the user's skills and emotional state.

[0715] (Application example 3)

[0716] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0717] Conventional training recommendation systems determine the necessary skills based on the user's job type and career history, and recommend training, but they are unable to take the user's emotional state into account. This makes it difficult to provide optimal training when the user is stressed or in a specific emotional state. Furthermore, since they are unable to evaluate skills or recommend training in real time, they lack immediacy. To solve these issues, a system that recognizes the user's emotional state and adjusts training accordingly is needed.

[0718] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0719] In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, means for acquiring video in real time, means for processing the acquired video, means for evaluating skills based on the processed video, means for recommending training based on the evaluation results, and means for displaying the recommended training. This makes it possible to provide optimal training according to the user's emotional state, and to evaluate skills and recommend training in real time.

[0720] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to an interface that allows the user to provide information about their occupation, past experience, and personal characteristics to the system.

[0721] The "means for determining the required skills based on the input information" is an algorithm or program that analyzes the information provided by the user and identifies the skills required by that user.

[0722] The "means for recommending training based on determined skills" is a function for suggesting training programs suitable for the user based on the identified skills.

[0723] "Means for recognizing user emotions" refers to technology for detecting and analyzing a user's emotional state from facial expressions, voice, etc.

[0724] "Means for adjusting training recommendations based on recognized emotions" is a function for changing training content and methods depending on the user's emotional state.

[0725] "Means for acquiring video in real time" refers to technology for capturing video of a user in real time using a device such as a camera.

[0726] The "means for processing the captured video" refers to an algorithm or program for analyzing the captured video data and extracting the necessary information.

[0727] The "means for evaluating skills based on processed video" is a function for evaluating a user's skill level based on analyzed video data.

[0728] "Means for recommending training based on evaluation results" is a function for suggesting training programs suitable for users based on the results of skill evaluation.

[0729] The "means for displaying recommended training" refers to a display or interface for visually presenting the recommended training content to the user.

[0730] As an embodiment of the present invention, a system will be described in which employees in a brick-and-mortar store wear smart glasses and receive real-time skill assessments and training recommendations.

[0731] System configuration

[0732] This system uses the following hardware and software:

[0733] Hardware: Smart glasses, camera

[0734] Software: OpenCV (image processing library), emotion_recognition (emotion recognition library), skill_assessment (skill assessment library), training_recommendation (training recommendation library)

[0735] Program processing

[0736] The server includes means for inputting information such as the user's occupation, career, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, means for acquiring video in real time, means for processing the acquired video, means for evaluating skills based on the processed video, means for recommending training based on the evaluation results, and means for displaying the recommended training.

[0737] Processing flow

[0738] 1. Entering user information: The user puts on the smart glasses and enters information such as occupation, career, and characteristics into the system.

[0739] 2. Skill determination: The server analyzes the input information and determines the skills required by the user.

[0740] 3. Emotion recognition: The camera captures the user's video in real time and uses an emotion recognition library to recognize the user's emotional state.

[0741] 4. Skill evaluation: Process the captured video and evaluate the user's skill level using the skill evaluation library.

[0742] 5. Training recommendation: Based on the results of skill assessment and emotion recognition, the training recommendation library recommends the most suitable training for the user.

[0743] 6. Training display: Recommended training content will be displayed on the smart glasses display.

[0744] Specific examples

[0745] For example, if a brick-and-mortar employee is feeling stressed while serving a customer, the emotion engine will recognize this and recommend training on stress management, while if the employee is feeling happy, it will recommend training on maintaining that emotion.

[0746] Prompt Sentence Examples

[0747] "What training would you recommend if an employee is experiencing stress while serving customers?"

[0748] In this way, store employees can efficiently improve their skills and receive optimal training according to their emotional state.

[0749] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0750] Step 1:

[0751] The user puts on the smart glasses and inputs information such as occupation, career, characteristics, etc.

[0752] Input: Information such as user's occupation, career, characteristics, etc.

[0753] Output: User information entered

[0754] Specific Actions: A user uses the smart glasses interface to input information about their job title, past experience, and personal characteristics.

[0755] Step 2:

[0756] The server analyzes the input information and determines the required skills.

[0757] Input: User information

[0758] Output: Required skills

[0759] Specific operation: The server analyzes the information entered by the user and uses a skill evaluation algorithm to identify the skills required by the user.

[0760] Step 3:

[0761] A camera is used to capture images of the user in real time.

[0762] Input: None (real-time video acquisition)

[0763] Output: Captured video data

[0764] Specific operation: The camera installed in the smart glasses captures the user's image in real time and transmits the image data to the server.

[0765] Step 4:

[0766] The server processes the captured video and recognizes the user's emotions.

[0767] Input: Captured video data

[0768] Output: Perceived emotional state

[0769] Specific operation: The server uses an emotion recognition library to analyze the user's facial expressions and voice from the captured video data and recognize their emotional state.

[0770] Step 5:

[0771] The server performs skill evaluation based on the processed video.

[0772] Input: Processed video data

[0773] Output: Skill evaluation results

[0774] Specific operation: The server uses the skill assessment library to analyze the processed video data and assess the user's skill level.

[0775] Step 6:

[0776] The server recommends training based on the results of skill evaluation and emotion recognition.

[0777] Input: Skill assessment results, perceived emotional state

[0778] Output: Recommended training content

[0779] Specific operation: The server uses the training recommendation library to integrate the skill evaluation results and emotion recognition results and generate the optimal training program for the user.

[0780] Step 7:

[0781] Recommended training content is displayed on the smart glasses display.

[0782] Input: Recommended training content

[0783] Output: Training content displayed on the smart glasses display

[0784] Specific operation: The server sends the recommended training content to the smart glasses and displays it on the screen, and the user follows the training.

[0785] 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.

[0786] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0787] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0788] 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.

[0789] [Second embodiment]

[0790] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0791] 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.

[0792] 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).

[0793] 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.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] In the smart glasses 214, 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.

[0801] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0802] "Example 1"

[0803] As one embodiment of the present invention, a system is provided that has an interface for users to input information such as their occupation, career history, characteristics, etc. This interface is designed to allow users to easily input information using text boxes, drop-down menus, etc.

[0804] "Example 2"

[0805] Next, a function is provided to determine the required skills based on the input information. This function uses machine learning algorithms and rule-based logic to determine the required skills from the user's information. For example, if the user is an engineer, it will determine that programming skills and project management skills are required.

[0806] "Example 3"

[0807] Finally, a training recommendation function will be provided based on the skills identified. This function recommends training that is individually optimized for each user. For example, if it is determined that a user needs programming skills, it will recommend training in Python or Java. This allows users to efficiently acquire the skills they need.

[0808] The processing flow of each embodiment will be described below.

[0809] "Example 1"

[0810] Step 1: The user enters information such as their job title, career history, and characteristics through the system interface. For example, the user enters information such as "Engineer," "5 years of experience," and "Java skills" into text boxes and drop-down menus.

[0811] "Example 2"

[0812] Step 1: The system takes the input and determines the required skills. This determination is made using machine learning algorithms and rule-based logic. For example, if a user inputs that they are an "engineer" and have "Java skills," the system might determine that they need skills such as "project management" and "database administration."

[0813] "Example 3"

[0814] Step 1: The system recommends training based on the skills identified. This recommendation is tailored to the individual user. For example, if the system determines that the skill "project management" is needed, it will recommend project management training.

[0815] Step 2: The user receives recommendations from the system and acquires the necessary skills by taking the recommended training. This allows the user to efficiently acquire the skills they need.

[0816] Example 1

[0817] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] In conventional systems, even if users input information such as their occupation, career history, and characteristics, it was difficult to effectively utilize that information to provide individually optimized career advice. In addition, there was a lack of a way to automatically determine the skills users needed and provide appropriate training and advice. This meant that users were unable to obtain specific guidance to effectively advance their career path.

[0819] 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.

[0820] In this invention, the server includes means for inputting information such as a user's occupation, career history, and characteristics, means for saving the input information in a database, means for processing and analyzing the saved information, means for generating prompt sentences based on the processed and analyzed information, means for sending the generated prompt sentences to a generative AI model, and means for providing the user with a response from the generative AI model, thereby enabling the user to receive individually optimized career advice based on their own information.

[0821] "User" refers to an individual who uses the system to input information such as their occupation, career history, and characteristics, and receives career advice.

[0822] "Occupation" refers to the type of work or duties that a user is engaged in.

[0823] "Career" refers to the job and work history a user has had.

[0824] "Features" refers to individual information such as a user's skills, expertise, and characteristics.

[0825] "Input means" refers to the interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[0826] "Database" refers to an information system for storing and managing information entered by users.

[0827] "Means for processing and analyzing" refers to the function of processing the information stored in the database and normalizing and filtering the data as necessary.

[0828] A "prompt sentence" refers to an instruction sentence for a generative AI model that is generated based on user input information.

[0829] A "generative AI model" refers to an artificial intelligence model that generates a response based on a prompt sentence based on user input information.

[0830] "Response" refers to the career advice or suggestions that the generative AI model generates based on the prompt.

[0831] "Means to provide" refers to the functionality for communicating responses from the generative AI model to the user.

[0832] This invention is a system that provides individually optimized career advice to users by inputting information such as their occupation, career history, and characteristics, and utilizing a generative AI model based on that information. Specific embodiments of this system are described below.

[0833] Hardware and software used

[0834] Hardware:

[0835] Terminal: The device through which a user enters information (e.g., PC, smartphone, tablet)

[0836] Server: A server for storing data, processing and analyzing it, and communicating with the generative AI model

[0837] software:

[0838] Database: A database system (e.g., MySQL, PostgreSQL) for storing user input information.

[0839] Generative AI model: An artificial intelligence model that generates responses based on user input (e.g., GPT-4).

[0840] Program processing

[0841] Users use their devices to access the system's interface and enter information such as their job title, career history, characteristics, etc. This interface includes UI components such as text boxes and drop-down menus, and is designed to allow users to easily enter information.

[0842] The terminal sends the information entered by the user to the server as an HTTP request, and the server stores the received information in a database and performs validation to maintain data integrity.

[0843] The server then processes and analyzes the stored information. Specifically, it standardizes the entered job titles and characteristics, and normalizes and filters the data as necessary. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0844] The server generates a prompt based on the processed and parsed information. For example, it generates the following prompt:

[0845] "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Could you please give me some career advice based on this information?"

[0846] The generated prompt is sent from the server to the generative AI model, which then generates an appropriate response based on the received prompt. For example, it might generate a response like, "Based on your experience and skill set, we recommend you move into cloud computing or data science as your next step."

[0847] Finally, the server sends the generated response to the device, where the user receives it, and receives specific career advice based on their job type and background.

[0848] Specific examples

[0849] For example, if a user enters the job title as "Software Engineer," experience of "5 years," and characteristics of "Python, Java," the following specific actions will occur:

[0850] 1. User enters "Software Engineer", "5 years", "Python, Java".

[0851] 2. The device sends this information to the server.

[0852] 3. The server stores the information in a database.

[0853] 4. The server normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0854] 5. The server generates the prompt: "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Based on this information, please give me some career advice that suits me."

[0855] 6. The server sends this prompt to the generative AI model.

[0856] 7. The generative AI model generates a response saying, "Based on your experience and skill set, we recommend your next steps be in cloud computing or data science."

[0857] 8. The server sends this response to the device.

[0858] 9. The user receives this response and gets advice on their next career step.

[0859] In this way, users can receive specific career advice based on their own information.

[0860] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0861] Step 1:

[0862] The user accesses the interface.

[0863] Input: A user accesses the system's interface using a terminal.

[0864] Output: The interface is displayed, ready for the user to enter information.

[0865] What happens: The user launches a web browser or mobile app and opens a system URL or app.

[0866] Step 2:

[0867] The user inputs information such as occupation, career history, characteristics, etc.

[0868] Input: Users enter information such as job title, experience, and characteristics into text boxes and drop-down menus.

[0869] Output: The input information is temporarily stored on the device.

[0870] Specific operation: The user enters information such as "Software Engineer", "5 years", "Python, Java" into the input fields.

[0871] Step 3:

[0872] The terminal sends the input information to the server.

[0873] Input: Information entered by the user.

[0874] Output: The input information is sent to the server as an HTTP request.

[0875] Specific operation: The terminal generates an HTTP request including the information entered by the user and sends it to the server.

[0876] Step 4:

[0877] The server stores the received information in a database.

[0878] Input: User input information sent from the device.

[0879] Output: The information is saved in a database.

[0880] Specific operation: The server stores the received information in a database (e.g., MySQL, PostgreSQL) and performs validation to maintain data integrity.

[0881] Step 5:

[0882] The server processes and analyzes the stored information.

[0883] Input: User information stored in the database.

[0884] Output: Processed and analyzed information.

[0885] Specific operation: The server analyzes the stored information and standardizes job titles and characteristics. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[0886] Step 6:

[0887] The server generates a prompt based on the processed and parsed information.

[0888] Input: Processed and analyzed user information.

[0889] Output: The generated prompt statement.

[0890] Specific behavior: Based on the user's information, the server generates a prompt statement: "I am a software engineer with 5 years of experience. I mainly use Python and Java. Based on this information, please provide me with suitable career advice."

[0891] Step 7:

[0892] The server sends the generated prompt to the generative AI model.

[0893] Input: The generated prompt statement.

[0894] Output: The prompt sent to the generative AI model.

[0895] Specific operation: The server sends the generated prompt sentence to a generative AI model (e.g., GPT-4).

[0896] Step 8:

[0897] A generative AI model generates a response.

[0898] Input: The prompt sent to the generative AI model.

[0899] Output: The generated response.

[0900] How it works: The generative AI model generates an appropriate response based on the prompt it receives, such as, "Based on your experience and skill set, I would recommend your next step be in cloud computing or data science."

[0901] Step 9:

[0902] The server sends the generated response to the terminal.

[0903] Input: The response from the generative AI model.

[0904] Output: The response sent to the terminal.

[0905] Specific operation: The server sends the response received from the generative AI model to the terminal.

[0906] Step 10:

[0907] The user receives the response.

[0908] Input: The response sent to the terminal.

[0909] Output: The response received by the user.

[0910] Specific operation: The user checks the response from the generative AI model through the device and receives advice on the next career step.

[0911] (Application example 1)

[0912] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0913] Conventional job information systems were unable to fully utilize information such as the user's job type, career history, and characteristics, making it difficult to provide users with the most suitable job information. Furthermore, they lacked the functionality to automatically determine the skills required by users and recommend appropriate training. This resulted in insufficient support for users to effectively advance their careers.

[0914] 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.

[0915] In this invention, the server includes means for inputting information such as the user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for generating optimal job information based on the input information, and means for providing job information using a generative AI model. This makes it possible to provide job information and training that are individually optimized for each user.

[0916] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc.

[0917] The "means for determining the required skills based on the input information" refers to a device or software that analyzes the information input by the user and automatically determines the skills the user needs based on that information.

[0918] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[0919] The "means for generating optimal job information based on input information" refers to a device or software that generates optimal job information for a user based on information input by the user.

[0920] A "means for providing job information using a generative AI model" is a device or software that uses a generative AI model to provide job information to users.

[0921] The system for implementing this invention provides an interface for users to input information such as their occupation, career history, characteristics, etc. The interface is implemented as a smartphone application and is designed to allow users to easily input information using text boxes and drop-down menus.

[0922] The server receives the information entered by the user and performs data analysis to determine the required skills. This analysis is performed using a Python program and OpenAI API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3) based on information such as the user's occupation, background, and characteristics.

[0923] The server then recommends appropriate training to the user based on the determined skills, providing training programs optimized for the user's skill level and job type.

[0924] The server then generates optimal job listings based on the information entered by the user. Using a generative AI model, the server provides optimal job listings to the user. In this process, the server sends the user's job type, career history, characteristics, and other information to the generative AI model as prompts to generate optimal job listings.

[0925] As a concrete example, consider the case where a user enters the following information:

[0926] Job title: Software Engineer

[0927] Experience: 5 years of development experience

[0928] Specialties: Team leader experience

[0929] In this case, the prompt sent to the generative AI model would be:

[0930] Job title: Software Engineer

[0931] Experience: 5 years of development experience

[0932] Specialties: Team leader experience

[0933] Please provide the best job information based on this.

[0934] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[0935] This system is realized using a smartphone, a server, and a generative AI model (e.g., GPT-3). The smartphone provides the user interface, and the server analyzes data and calls the generative AI model. The generative AI model generates optimal job listings based on user input.

[0936] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0937] Step 1:

[0938] A user uses a smartphone device to input information such as occupation, career history, and characteristics. The input interface includes text boxes and drop-down menus, and is designed to allow users to easily input information. The input information is then sent from the device to a server.

[0939] Input: Information such as user's occupation, career, characteristics, etc.

[0940] Output: User information sent to the server

[0941] Step 2:

[0942] The server analyzes the received user information and determines the required skills. This analysis is performed using a Python program and OpenAI API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3) based on information such as the user's occupation, career history, and characteristics.

[0943] Input: User information

[0944] Output: List of required skills

[0945] Step 3:

[0946] The server recommends appropriate training to the user based on the determined skills, the recommended training being optimized for the user's skill level and job type.

[0947] Input: List of required skills

[0948] Output: Recommended training program

[0949] Step 4:

[0950] The server generates optimal job information based on the information entered by the user. The generative AI model is used to provide optimal job information to the user. In this process, information such as the user's job type, career history, and characteristics is sent to the generative AI model as a prompt sentence, which generates optimal job information.

[0951] Input: User information

[0952] Output: Generated job listings

[0953] Step 5:

[0954] The server then sends the generated job information to the user's smartphone, allowing the user to check the most suitable job information on the smartphone.

[0955] Input: Generated job posting

[0956] Output: Job information displayed on the user's smartphone

[0957] As a specific example of operation, consider the case where the user inputs the following information:

[0958] Job title: Software Engineer

[0959] Experience: 5 years of development experience

[0960] Specialties: Team leader experience

[0961] In this case, the prompt sent to the generative AI model would be:

[0962] Job title: Software Engineer

[0963] Experience: 5 years of development experience

[0964] Specialties: Team leader experience

[0965] Please provide the best job information based on this.

[0966] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[0967] Example 2

[0968] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0969] Previous systems had the problem that it was difficult for users to identify the skills they needed based on their job type and career history, and there was a lack of information to receive appropriate training.In addition, there was a lack of means to recommend individually optimized training to users, which led to the issue of users not being able to improve their skills efficiently.

[0970] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for receiving and preprocessing the input information, a means for determining the required skills based on the preprocessed information, and a means for returning the determined skills to the user. This enables the user to automatically identify the required skills based on their occupation and career, and receive individually optimized training.

[0971] "User" means any individual or entity that uses the System.

[0972] "Occupation" is information that indicates the type of occupation the user is engaged in.

[0973] "Career" refers to historical information such as the user's past work experience and educational background.

[0974] "Characteristics" are a user's skills, characteristics, and other individual information.

[0975] The "means for inputting" is an interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[0976] "Means for receiving" refers to the function by which the server receives information sent by the user.

[0977] "Preprocessing" is the process of converting received information into a format that can be understood by a machine learning model.

[0978] The "means of determining" is a function that identifies the required skills based on preprocessed information.

[0979] "Means of return" is a function that notifies the user of the determined skill.

[0980] A "machine learning algorithm" is a computational method for learning patterns from data and making predictions and classifications.

[0981] "Rule-based logic" is a method of making decisions based on predefined rules.

[0982] "Training" is the process of educating and training users to acquire the necessary skills.

[0983] "Individually optimized training" means an education or training program that is customized to a user's specific needs and circumstances.

[0984] MODE FOR CARRYING OUT THE INVENTION

[0985] This invention is a system that allows users to identify the skills they need based on their job type and career history, and receive individually optimized training. This system is implemented using the following hardware and software.

[0986] Hardware and software used

[0987] Hardware: A server with a powerful CPU and sufficient memory

[0988] Software: Python, TensorFlow (machine learning library), Scikit-learn (machine learning library), Flask (web framework)

[0989] System Overview

[0990] 1. Enter your user information:

[0991] The user uses the terminal to input information such as his / her occupation, career history, characteristics, etc.

[0992] Example: User types "Engineer."

[0993] 2. Data reception and preprocessing:

[0994] The server receives the information sent by the user.

[0995] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[0996] 3. Skill Assessment:

[0997] The server inputs the preprocessed data into the machine learning model.

[0998] It uses machine learning models (using TensorFlow and Scikit-learn) and rule-based logic to determine the skills a user needs.

[0999] Example: If the user types "engineer," the model determines that "programming skills" and "project management skills" are also required.

[1000] 4. Returning the results:

[1001] The server returns the determined skill to the user.

[1002] For example, it says, "You need programming and project management skills."

[1003] Examples and prompts

[1004] Examples:

[1005] If a user types in "data scientist," the system will determine that "data analysis skills" and "machine learning skills" are required.

[1006] If a user types in "marketing manager," the system will determine that "digital marketing skills" and "data analysis skills" are required.

[1007] Example prompt sentence:

[1008] "Please enter your occupation: e.g., Engineer, Data Scientist, Marketer"

[1009] "Please tell us your current skills: e.g., Python, project management, data analysis"

[1010] The system automatically determines the required skills by having users input information about their job type and skills, and provides them to users, allowing them to clearly understand the skills required for their career path.

[1011] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1012] Step 1:

[1013] The user uses the terminal to enter information such as their occupation, career history, and characteristics.

[1014] Specifically, the user opens a browser on the device, accesses the system's web page, enters "engineer" in the occupation input field, and clicks the submit button.

[1015] Input: User's occupation, career, characteristics, etc.

[1016] Output: The information entered by the user is sent to the server

[1017] Step 2:

[1018] The server receives the information sent by the user.

[1019] Specifically, the server receives an HTTP request and obtains the user's input data.

[1020] Input: User-submitted information

[1021] Output: User information received by the server

[1022] Step 3:

[1023] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[1024] Specifically, the server tokenizes the text data and converts it into numeric data as needed.

[1025] Input: User information received by the server

[1026] Output: Preprocessed data

[1027] Step 4:

[1028] The server inputs the preprocessed data into the machine learning model.

[1029] Specifically, the server uses TensorFlow and Scikit-learn to input data into the model.

[1030] Input: Preprocessed data

[1031] Output: Skill assessment results from the machine learning model

[1032] Step 5:

[1033] The server uses machine learning models and rule-based logic to determine the skills a user needs.

[1034] Specifically, the model outputs "programming skills" and "project management skills" in response to the input "engineer."

[1035] Input: Preprocessed data

[1036] Output: List of required skills

[1037] Step 6:

[1038] The server returns the determined skill to the user.

[1039] Specifically, the server generates an HTTP response that tells the user, "You need programming skills and project management skills."

[1040] Input: List of required skills

[1041] Output: Skill information returned to the user

[1042] In this way, users can identify the skills they need based on their job type and background and receive individually optimized training.

[1043] (Application example 2)

[1044] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1045] While conventional systems can determine the required skills based on the user's job type and career history and recommend training, they have the problem of not being able to assign optimal tasks to factory workers. There was also a need for a system that could assign optimal tasks based on workers' years of experience, qualifications, and past performance evaluations.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1047] In this invention, the server includes means for inputting information such as the user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information on workers in the factory and assigning optimal tasks. This makes it possible not only to recommend individually optimized training to the user, but also to assign optimal tasks to workers in the factory.

[1048] "User" refers to an individual or organization that uses the System.

[1049] "Job type" refers to the type of work or job a user is engaged in.

[1050] "Career" refers to a user's past work experience, educational background, qualifications, etc.

[1051] "Characteristics" refers to a user's individual characteristics, such as skills, abilities, and personality.

[1052] "Means for inputting information" refers to a method or device for inputting information such as a user's occupation, career, characteristics, etc. into the system.

[1053] "Means for determining required skills" refers to a method or device for identifying the skills required by a user based on input information.

[1054] "Training recommendation means" refers to a method or device for suggesting appropriate training to a user based on the determined skills.

[1055] "Factory workers" refers to employees working in a factory.

[1056] "Worker information" refers to data such as years of experience, qualifications, and past performance evaluations of workers in the factory.

[1057] The "means for assigning optimal work" refers to a method or device for determining and assigning the most suitable work to each worker based on information about the worker.

[1058] A system for implementing this invention includes means for inputting information such as a user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information about workers in a factory and assigning them the most appropriate work.

[1059] System configuration

[1060] The system consists of the following main components:

[1061] 1. User information input terminal: A terminal for inputting information such as the user's occupation, career, characteristics, etc. For example, a PC or tablet is used.

[1062] 2. Server: Processes the input information, determines the skills needed, recommends training, and processes information on workers in the factory to assign them the most appropriate tasks.

[1063] 3. Database: A database for storing user information, worker information, skill information, training information, etc.

[1064] 4. Machine learning algorithm: An algorithm to determine the required skills based on user information and assign optimal tasks. For example, RandomForestClassifier from the scikit-learn library is used.

[1065] Processing flow

[1066] 1. Input of user information: The user inputs information such as occupation, career, characteristics, etc. into the system through an input terminal.

[1067] 2. Information processing: The server stores the input information in a database and uses machine learning algorithms to determine the required skills.

[1068] 3. Training Recommendation: The server recommends the most appropriate training for the user based on the determined skills.

[1069] 4. Entering worker information: Enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals.

[1070] 5. Work allocation: The server allocates the optimal work based on the information of the workers.

[1071] Hardware and software used

[1072] Hardware: PCs, tablets, servers, database servers

[1073] Software: pandas library, scikit-learn library, database management system (e.g., MySQL)

[1074] Specific examples

[1075] For example, when a new worker joins a factory, the worker's years of experience, qualifications, and past performance evaluations are entered into the system via an input terminal. Based on this information, the server uses machine learning algorithms to assign the most suitable tasks.

[1076] Example prompt sentence:

[1077] Enter the new worker's information. Enter the number of years of experience, the number of certifications, and past performance ratings. For example, if the worker has 5 years of experience, 3 certifications, and a past performance rating of 4, enter the following:

[1078] Years of experience: 5

[1079] Number of qualifications: 3

[1080] Past performance rating: 4

[1081] In this way, the system can not only recommend individually optimized training for users, but also assign optimal tasks to workers within the factory.

[1082] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1083] Step 1:

[1084] Users input information such as their occupation, career, and characteristics into the system through an input terminal. The input information is sent to the server and stored in a database. The input data includes the user's occupation, career, and characteristics.

[1085] Step 2:

[1086] The server retrieves user information stored in a database and uses a machine learning algorithm to determine the required skills. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the required skills based on the user's occupation and background. The input data is user information, and the output data is the required skills.

[1087] Step 3:

[1088] The server recommends the most suitable training for the user based on the determined skills. The recommended training information is sent to the user's device. The input data is the required skills, and the output data is the recommended training.

[1089] Step 4:

[1090] Users enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals. The entered information is sent to the server and stored in the database. The input data includes the worker's years of experience, qualifications, past performance evaluations, etc.

[1091] Step 5:

[1092] The server retrieves worker information stored in a database and uses a machine learning algorithm to assign the optimal task. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the optimal task based on the worker's years of experience, qualifications, and past performance evaluations. The input data is worker information, and the output data is the optimal task.

[1093] Step 6:

[1094] The server sends the assigned work information to the worker's terminal. The worker checks and executes the assigned work through the terminal. The input data is the optimal work, and the output data is the work information displayed on the worker's terminal.

[1095] Example 3

[1096] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1097] With conventional skill assessment systems, it was difficult to provide individually optimized training programs when determining the skills required based on the user's job type and career history. Furthermore, because user skill assessment was performed manually, there was a problem of reduced accuracy and efficiency. This made it difficult for users to efficiently acquire the skills they needed.

[1098] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1099] In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for transmitting the input information to the server, a means for the server to evaluate the user's skills using a generative AI model, a means for generating a training program based on the evaluation results, and a means for providing the generated training program to the user. This makes it possible to automatically evaluate the user's skills and provide an individually optimized training program.

[1100] "User" means an individual or organization that uses the System to receive a skill assessment or training program recommendation.

[1101] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[1102] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[1103] "Characteristics" refers to information that refers to a user's individual characteristics, such as skills, abilities, interests, and personality.

[1104] "Means for input" refers to the interface or device that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[1105] "Means for sending" refers to the communication means for sending the information entered by the user to the server.

[1106] "Server" refers to a computer system that receives and processes information sent by users.

[1107] A "generative AI model" refers to an artificial intelligence model that analyzes user information and performs skill evaluations and generates training programs.

[1108] "Means of evaluation" refers to the process or algorithm used to evaluate a user's skills using a generative AI model.

[1109] "Training Program" refers to the content of education and training that enables users to acquire the skills they need.

[1110] "Means for providing" refers to an interface or device for presenting the generated training program to the user.

[1111] This invention is a system that inputs information such as a user's occupation, career history, and characteristics, evaluates the required skills based on that information, and recommends the most appropriate training program. Specific embodiments of this system are described below.

[1112] First, the user enters information such as their occupation, career history, and characteristics into the device. The device then sends the entered information to the server. At this time, the device converts the information into JSON format and transmits it securely using the HTTPS protocol.

[1113] The server analyzes the received information and evaluates the user's skill using a generative AI model, such as OpenAI's GPT-4. The server inputs the following prompt into the generative AI model:

[1114] Assess users' skills and recommend the best training program for them. User input includes:

[1115] "I have basic programming skills, but I lack data science knowledge."

[1116] The generative AI model analyzes the prompt and evaluates the user's skills. Based on the evaluation results, the server generates the optimal training program for the user, for example, recommending a Python data analysis course to learn the basics of data science.

[1117] The server sends the generated training program to the terminal. The data is also encrypted when sent. The terminal displays the received training program to the user. The user can check the displayed training program and take it if necessary.

[1118] This system allows users to efficiently evaluate their skills and receive individually optimized training programs. This allows users to efficiently acquire the necessary skills. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1119] Step 1:

[1120] The user inputs information such as occupation, career history, characteristics, etc.

[1121] The user enters information such as their occupation, career history, and characteristics into the input form on the device. For example, they might enter, "I have basic programming skills, but I lack knowledge of data science." The entered information is temporarily stored in the device's memory.

[1122] Step 2:

[1123] The terminal transmits the input information to the server.

[1124] The terminal converts the information entered by the user into JSON format and sends it to the server using the HTTPS protocol. The input data is a JSON object containing information such as job type, career history, and characteristics. The transmitted data is received by the server's receiving module.

[1125] Step 3:

[1126] The server evaluates the user's skills using a generative AI model.

[1127] The server parses the received JSON data and inputs a prompt sentence to the generative AI model. The prompt sentence is generated as follows:

[1128] Assess users' skills and recommend the best training program for them. User input includes:

[1129] "I have basic programming skills, but I lack data science knowledge."

[1130] The generative AI model analyzes the prompt and evaluates the user's skills, outputting the results as data including the skills and level required by the user.

[1131] Step 4:

[1132] The server generates a training program based on the evaluation results.

[1133] The server searches the database for an appropriate training program based on the evaluation results obtained from the generative AI model. For example, it may select a Python data analysis course to learn the basics of data science. The generated training program is output as a JSON object containing content optimized for the user.

[1134] Step 5:

[1135] The server transmits the generated training program to the terminal.

[1136] The server converts the generated training program into JSON format and sends it to the device using the HTTPS protocol. The transmitted data is received by the device's receiving module.

[1137] Step 6:

[1138] The terminal displays the training program to the user.

[1139] The device parses the received JSON data and displays the contents of the training program to the user. The user can check the displayed training program and click the "Take Part" button if necessary. The displayed content includes details of the training and how to take part.

[1140] (Application example 3)

[1141] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1142] Conventional skill assessment systems can determine the skills required based on a user's job type and career history and recommend training, but they have the problem of not being able to assess skills in real time or recommend training based on that assessment. This makes it difficult for users to acquire the skills they actually need in a timely manner, and makes it difficult to improve their skills efficiently.

[1143] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results. This makes it possible to evaluate the user's skills in real time and recommend optimal training based on the evaluation.

[1144] "User" refers to any individual or entity that uses the System.

[1145] "Job type" refers to the type of work or role a user has.

[1146] "Career" refers to a user's past work experience, educational background, and other history.

[1147] "Traits" refer to the individual attributes of a user, such as their skills, abilities, interests, and personality.

[1148] "Means for inputting information" refers to an interface or device for inputting information such as the user's occupation, career, characteristics, etc. into the system.

[1149] "Means for determining required skills" refers to algorithms or programs that identify the skills a user needs based on input information.

[1150] "Training recommendation means" refers to a function that suggests appropriate training to users based on their assessed skills.

[1151] "Real-time assessment means" refers to sensors, data analysis algorithms, or programs that assess a user's skills in real time.

[1152] "Means for recommending training based on evaluation results" refers to a function for suggesting optimal training to users based on skill evaluation results obtained in real time.

[1153] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results.

[1154] System program generation

[1155] The server provides an interface for users to input information such as their occupation, career, and characteristics. Users input this information using smart glasses or a smartphone. The input information is sent to the server and stored in a database.

[1156] The server then runs an algorithm to determine the required skills based on the input information. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills.

[1157] Furthermore, the server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors in the smart glasses monitor the user's movements and send the data to the server, which then analyzes the data and evaluates the user's skill level.

[1158] Based on the evaluation results, the server will recommend optimal training to the user. For example, if the robot is not operating smoothly, the server will display a message on the smart glasses saying, "Robot operation training is recommended."

[1159] Hardware and software used

[1160] Hardware: smart glasses, smartphone, server

[1161] Software: Database management system, data analysis algorithms, user interface

[1162] Specific examples

[1163] Consider a scenario in which a factory robot operator is wearing smart glasses. When the operator starts operating the robot, the smart glasses monitor the operation and evaluate the operator's skills. For example, if the operator is not operating the robot smoothly, the smart glasses will display a message saying, "Robot operation training is recommended."

[1164] Prompt Sentence Examples

[1165] Develop an application that evaluates a user's skills and recommends necessary training. The user is a factory robot operator and wears smart glasses. Skill evaluation is performed in real time, and training recommendations are made based on the evaluation results. Specific skills include robot operation, maintenance, and programming. Training recommendations should be made for skills with an evaluation score of less than 5.

[1166] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1167] Step 1:

[1168] Users use smart glasses or smartphones to input information such as their occupation, career history, and characteristics. The input information is sent from the device to a server, which then stores the information in a database. The input data includes the user's occupation (e.g., factory robot operator), career history (e.g., 5 years of experience), and characteristics (e.g., programming skills).

[1169] Step 2:

[1170] The server runs an algorithm that determines the required skills based on the information stored in the database. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills. The input data is the user information, and the output data is a list of required skills.

[1171] Step 3:

[1172] The server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors installed in the smart glasses monitor the user's movements and send the data to the server. The server analyzes this data and evaluates the user's skill level. The input data is sensor data, and the output data is the skill evaluation result.

[1173] Step 4:

[1174] The server recommends optimal training to the user based on the evaluation results. For example, if the user's robot operation is not smooth, the server displays a message on the smart glasses saying, "We recommend training to operate the robot." The input data is the skill evaluation result, and the output data is a training recommendation message.

[1175] Step 5:

[1176] The user receives training recommendation messages from the server and starts the necessary training. The user reports the training progress to the server via smart glasses or a smartphone. The server stores the training progress in a database and monitors the improvement of the user's skill level. The input data is the training progress, and the output data is the updated skill evaluation results.

[1177] 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.

[1178] "Example 1"

[1179] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[1180] "Example 2"

[1181] The emotion engine also determines the skills required based on the user's emotions. For example, if the emotion engine recognizes that the user is feeling angry, it determines that communication skills and problem-solving skills are required. This makes it possible to acquire skills according to the user's emotional state.

[1182] "Example 3"

[1183] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[1184] The processing flow of each embodiment will be described below.

[1185] "Example 1"

[1186] Step 1: The emotion engine recognizes the user's emotion.

[1187] Step 2: Adjust training recommendations based on the emotions recognized by the emotion engine.

[1188] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will recommend relaxation and stress management training.

[1189] Step 4: If the emotion engine also detects that the user is experiencing joy, it will recommend training to maintain that joy.

[1190] "Example 2"

[1191] Step 1: The emotion engine recognizes the user's emotion.

[1192] Step 2: Determine the skills required based on the emotions recognized by the emotion engine.

[1193] Step 3: For example, if the emotion engine detects that the user is angry, it determines that communication skills and problem-solving skills are needed.

[1194] Example 1

[1195] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1196] Conventional training recommendation systems can determine the necessary skills based on the user's job type and career history and recommend training, but they do not take into account the user's emotional state when recommending training. As a result, they are unable to provide optimal training according to the user's emotional state, which can lead to reduced user satisfaction and effectiveness.

[1197] 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.

[1198] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for receiving the input information and storing it in a database, means for analyzing the user's emotions based on the stored information, and means for recommending training based on the analyzed emotions, thereby making it possible to recommend optimal training according to the user's emotional state.

[1199] "User" refers to an individual or organization who uses the system to input information such as their occupation, career history, and characteristics, and receives training recommendations.

[1200] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[1201] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[1202] "Characteristics" refers to information that refers to a user's personal characteristics, skills, interests, etc.

[1203] "Input means" refers to an interface that allows a user to provide information such as their occupation, career, characteristics, etc. to the system.

[1204] "Database" refers to a storage device or system for organizing and storing information entered by users.

[1205] "Means of emotion analysis" refers to technologies and algorithms used to recognize and analyze emotions from user input.

[1206] "Training recommendation means" refers to a function for suggesting suitable training to a user based on the analyzed emotions.

[1207] "Natural language processing technology" refers to technology for analyzing meaning and emotions from text data entered by users.

[1208] MODE FOR CARRYING OUT THE INVENTION

[1209] This invention is a system that allows a user to input information such as their occupation, career, and characteristics, analyzes emotions based on that information, and recommends optimal training. Specific embodiments of this system are described below.

[1210] 1. Program Generation

[1211] The server provides an interface for the user to enter information. This interface includes text boxes and drop-down menus designed to make it easy for the user to enter information.

[1212] 2. Program Processing

[1213] The server receives the information entered by the user and stores it in a database, which organizes and stores information such as the user's occupation, career, and characteristics.

[1214] The server then uses an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing (NLP) technology to analyze emotions from the user's input. Specifically, it uses "natural language processing technology" to extract emotions from the user's text.

[1215] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user enters "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend relaxation and stress management training.

[1216] 3. Specific Examples

[1217] As a specific example, consider the case where a user inputs "I've been busy at work lately and I'm feeling stressed."

[1218] 1. Using the device interface, the user types "I've been busy and stressed out lately" into a text box.

[1219] 2. The server receives this information and stores it in a database as "Occupation: Office Worker, Emotion: Stress."

[1220] 3. The server uses an emotion engine (e.g., natural language processing technology) to analyze the emotion "stress."

[1221] 4. Based on the analysis results, the server recommends "relaxation and stress management training."

[1222] 5. Users undergo recommended training and practice specific methods to reduce stress (e.g., deep breathing, meditation).

[1223] Prompt Sentence Examples

[1224] If a user types, "I've been busy and stressed lately at work," how would the emotion engine respond and what training would it recommend?

[1225] In this way, an optimal workout is provided depending on the user's emotional state.

[1226] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1227] Step 1:

[1228] The user enters information

[1229] The user uses the terminal interface to input information such as their occupation, career, and characteristics. The input information is sent to the server through text boxes and drop-down menus. The input data includes text information such as occupation, career, and characteristics.

[1230] Step 2:

[1231] The server receives the information and stores it in a database

[1232] The server receives the information sent by the user and stores it in a database. Specifically, it analyzes the received text data and stores it in the appropriate fields. For example, it may be stored in a format such as "Occupation: Company employee," "Career: 5 years of experience," or "Characteristics: Low stress tolerance." The input data is text information, and the output data is structured data stored in the database.

[1233] Step 3:

[1234] The server uses an emotion engine to analyze the user's emotions.

[1235] The server starts an emotion engine based on the stored information. The emotion engine uses natural language processing technology to analyze emotions from the user's input. For example, natural language processing technology can be used to extract emotions such as "stress" or "joy" from the user's text. The input data is text information, and the output data is analyzed emotional information.

[1236] Step 4:

[1237] The server recommends training based on the analysis results.

[1238] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user inputs "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend training related to relaxation and stress management. The input data is the analyzed emotion information, and the output data is the recommended training content.

[1239] Step 5:

[1240] Users receive recommended training

[1241] The user receives training recommendations from the server. The training content is optimized according to the user's emotional state, providing specific methods for reducing stress and maintaining joy. For example, relaxation techniques such as deep breathing and meditation are recommended. The input data is the recommended training content, and the output data is the user's practice results.

[1242] (Application example 1)

[1243] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1244] While conventional systems can determine the necessary skills based on a user's job type and career history and recommend training, they have the problem of not being able to adjust training or workload taking into account the user's emotional state, making it difficult to provide optimal training and work environments that take into account the user's stress and emotional state.

[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 inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state. This makes it possible to provide optimal training and a working environment according to the user's emotional state.

[1247] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc. into the system.

[1248] "Means for determining required skills based on input information" refers to a device or software that analyzes information such as job type, career history, and characteristics input by the user and automatically determines the skills the user requires.

[1249] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[1250] "Means for recognizing user emotions" refers to a device or software that analyzes the user's facial expressions, voice, actions, etc., and recognizes the user's emotional state.

[1251] A "means for adjusting training recommendations based on recognized emotions" is a device or software that adjusts the content or method of training based on the recognized emotional state of the user.

[1252] The "means for adjusting the workload according to the user's emotional state" refers to a device or software that adjusts the pace or workload of work according to the user's emotional state.

[1253] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state.

[1254] Generating a Program

[1255] The program of this system is configured as follows:

[1256] 1. Enter your user information:

[1257] The server provides an interface for users to enter information about their job title, background, characteristics, etc. This may include text boxes and drop-down menus.

[1258] 2. Determine the skills needed:

[1259] The server analyzes the input information and automatically determines the skills the user needs, using machine learning algorithms and database searches.

[1260] 3. Training Recommendations:

[1261] Based on the skills determined, the server recommends appropriate training to the user, which is optimized for the user's job type and background.

[1262] 4. Emotion Recognition:

[1263] The server analyzes the user's facial expressions, voice, and behavior to recognize the user's emotional state. This analysis is performed using an emotion recognition engine (EmotionEngine).

[1264] 5. Training Adjustments:

[1265] The server then adjusts the content and method of training based on the user's perceived emotional state, for example recommending relaxation training if the user is feeling stressed.

[1266] 6. Workload adjustment:

[1267] The server adjusts the pace and workload of the work according to the user's emotional state, for example, slowing down the work pace if the user is feeling stressed.

[1268] Hardware and software used

[1269] Hardware: Factory robots, emotion-recognizing cameras, speakers

[1270] Software: EmotionEngine (emotion recognition engine), Robot (robot control library)

[1271] Specific examples

[1272] For example, if the emotion engine detects that a factory worker is feeling stressed from working long hours, the server can instruct the robot to slow down its work pace and play relaxation music, reducing the operator's stress and improving work efficiency.

[1273] Prompt Sentence Examples

[1274] Examples of prompts for a generative AI model might include:

[1275] If the user is feeling stressed, the robot should generate a program to slow down its work pace and play relaxation music.

[1276] In this way, it is possible to provide an optimal training or working environment according to the user's emotional state.

[1277] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1278] Step 1:

[1279] The server provides an interface for users to enter information such as their job title, background, characteristics, etc. Users enter information using text boxes and drop-down menus. The entered information is sent to the server and stored in a database.

[1280] Input: User's occupation, career, characteristics, etc.

[1281] Output: User information stored in the database

[1282] Step 2:

[1283] The server analyzes user information stored in a database and automatically determines the skills the user needs, using machine learning algorithms and database searches.

[1284] Input: User information stored in the database

[1285] Output: A list of skills the user needs

[1286] Step 3:

[1287] Based on the skills determined, the server recommends appropriate training to the user, which is optimized for the user's job type and background.

[1288] Input: A list of skills the user needs

[1289] Output: A list of recommended workouts

[1290] Step 4:

[1291] The server analyzes the user's facial expressions, voice, and behavior to recognize the user's emotional state. This analysis is performed using an emotion recognition engine (EmotionEngine).

[1292] Input: User facial, voice, and behavioral data

[1293] Output: User's emotional state

[1294] Step 5:

[1295] The server then adjusts the content and method of training based on the user's perceived emotional state, for example recommending relaxation training if the user is feeling stressed.

[1296] Input: User's emotional state, list of recommended workouts

[1297] Output: List of adjusted trainings

[1298] Step 6:

[1299] The server adjusts the pace and workload of the work according to the user's emotional state, for example, slowing down the work pace if the user is feeling stressed.

[1300] Input: User's emotional state

[1301] Output: Adjusted work pace or load

[1302] In this way, it is possible to provide an optimal training or working environment according to the user's emotional state.

[1303] Example 2

[1304] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1305] While conventional skill assessment systems can determine the skills required based on a user's occupation and career history, they are unable to consider the user's emotional state. As a result, they are unable to determine appropriate skills or recommend training based on the user's emotional state, which means they are unable to fully meet the user's needs.

[1306] 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.

[1307] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for determining required skills based on the input information, means for analyzing the user's emotional state, means for determining required skills based on the emotional state, and means for recommending training based on the determined skills. This makes it possible to determine skills and recommend training that take into account not only the user's occupation and career, but also their emotional state.

[1308] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to the interface that allows the user to input information such as their occupation, past experience, skills, and characteristics into the system.

[1309] "Means for determining required skills based on input information" refers to algorithms and logic that analyze information input by the user, such as job type and career history, and identify the skills the user needs based on that information.

[1310] "Means for analyzing the user's emotional state" refers to technologies or engines for recognizing emotions from text or voice data entered by the user and analyzing that emotional state.

[1311] "Means for determining required skills based on emotional state" refers to algorithms or logic for identifying the skills a user needs based on the analyzed emotional state of the user.

[1312] "Training recommendations based on determined skills" refers to a system or functionality that suggests appropriate training or learning resources to users based on the identified skills needs.

[1313] This invention is a system that determines the necessary skills based on information input by the user, and further analyzes the emotional state of the user to recommend appropriate skills. Specific embodiments for implementing this system are described below.

[1314] Hardware and software used

[1315] Hardware: Servers, terminals (PCs, smartphones)

[1316] Software: Machine learning libraries (TensorFlow, scikit-learn), emotion recognition engines (e.g., IBM Watson Tone Analyzer)

[1317] System Overview

[1318] 1. Enter your user information

[1319] A user uses a terminal to input his / her own information (occupation, skills, emotional state, etc.). For example, a user inputs information such as "engineer" or "angry" into an input form on the terminal.

[1320] 2. Transmission of information

[1321] The terminal transmits the input information to the server. Specifically, the terminal transmits the user input information to the server using an HTTP request.

[1322] 3. Analysis of Information

[1323] The server analyzes the information it receives and uses machine learning algorithms and rule-based logic to determine the skills needed based on the user's occupation and skills. For example, the server might use TensorFlow and scikit-learn to determine that a user's occupation as an "engineer" requires programming and project management skills.

[1324] 4. Emotion Analysis

[1325] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes emotions from the text and voice data entered by the user. For example, the server uses IBM Watson Tone Analyzer to recognize the user's emotion of "anger" and determines that communication skills and problem-solving skills are required.

[1326] 5. Providing results

[1327] The server sends the analysis results to the device. The device then displays a list of required skills to the user. Specifically, the server uses an HTTP response to send the analysis results to the device, and the device displays "Required skills: programming skills, project management skills, communication skills, problem-solving skills" to the user.

[1328] Examples of concrete examples and prompts

[1329] Specific examples

[1330] If the user is an engineer and inputs that their current emotional state is "anger," the system determines that programming skills, project management skills, communication skills, and problem-solving skills are required. The user enters "engineer" and "anger" into the input form on the device and clicks the send button. The device sends the information to the server, which analyzes it and returns the results to the device. The device displays the results to the user.

[1331] Prompt Sentence Examples

[1332] "I'm an engineer and I'm currently experiencing anger. What skills do I need?"

[1333] This system allows users to efficiently acquire skills that correspond to their occupation and emotional state.

[1334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] Entering user information

[1337] The user uses the terminal to input his / her own information (occupation, skills, emotional state, etc.).

[1338] Input: The user enters information such as "engineer" or "anger" into the device's input form.

[1339] Specific actions: The user uses the device's keyboard or touchscreen to enter the required information into the input form and clicks the submit button.

[1340] Step 2:

[1341] Sending information

[1342] The terminal transmits the input information to the server.

[1343] Input: User-entered information (occupation, skills, emotional state)

[1344] Output: User information sent to the server

[1345] Specific operation: The device uses an HTTP request to send the user's input information to the server.

[1346] Step 3:

[1347] Analysis of information

[1348] The server analyzes the information it receives and uses machine learning algorithms and rule-based logic to determine the skills needed based on the user's occupation and skills.

[1349] Input: User information received by the server (occupation, skills)

[1350] Output: List of required skills

[1351] What it does: The server uses TensorFlow and scikit-learn to determine that the user's occupation is "engineer" and that they need programming and project management skills.

[1352] Step 4:

[1353] Emotion Analysis

[1354] The server analyzes the user's emotional state using an emotion engine, which recognizes emotions from the text and voice data entered by the user.

[1355] Input: User emotion information received by the server

[1356] Output: List of required skills based on emotional state

[1357] Specific operation: The server uses IBM Watson Tone Analyzer to recognize the user's emotion of "anger" and determines that communication skills and problem-solving skills are required.

[1358] Step 5:

[1359] Providing results

[1360] The server sends the analysis results to the device, which then displays a list of required skills to the user.

[1361] Input: List of required skills parsed by the server

[1362] Output: A list of required skills displayed on the device

[1363] Specific operation: The server sends the analysis results to the device using an HTTP response. The device displays the user's "Required skills: programming skills, project management skills, communication skills, problem-solving skills."

[1364] (Application example 2)

[1365] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1366] While conventional skill assessment systems can determine the necessary skills based on a user's job type and career history, they are unable to take the user's emotional state into account, making it difficult to provide appropriate skills in response to the user's emotions. Robots working in factories are also required to sense the emotional state of workers and provide appropriate skills, but conventional systems were unable to achieve this.

[1367] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1368] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for determining required skills based on the input information, means for sensing the user's emotional state, means for determining required skills based on the sensed emotional state, and means for recommending training based on the determined skills. This makes it possible to provide appropriate skills that take into account not only the user's occupation and career but also their emotional state.

[1369] "User" refers to an individual or organization that uses the System.

[1370] "Job type" refers to the type of work or role a user has.

[1371] "Career" refers to the user's history, including the jobs and educational background they have had.

[1372] "Traits" refer to individual attributes such as a user's skills, personality, interests, etc.

[1373] "Means for inputting information" refers to a method or device for inputting information such as a user's occupation, career, characteristics, etc. into the system.

[1374] "Means for determining required skills" refers to a method or device for identifying the skills a user needs based on input information.

[1375] "Means for sensing emotional state" refers to a method or device for detecting a user's emotions.

[1376] "Means for determining required skills based on a sensed emotional state" refers to a method or apparatus for identifying required skills based on a sensed emotional state of a user.

[1377] "Training recommendation means" refers to a method or device for suggesting appropriate training to a user based on the determined skills.

[1378] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining required skills based on the input information, means for sensing the user's emotional state, means for determining required skills based on the sensed emotional state, and means for recommending training based on the determined skills.

[1379] System program generation

[1380] The system executes the following programs. First, it provides a user interface as a means for inputting information such as the user's occupation, career, and characteristics. This interface includes forms and options for the user to enter their information. Next, it uses machine learning algorithms and rule-based logic to determine the required skills based on the input information. Furthermore, it performs emotion recognition using hardware such as a camera and microphone to sense the user's emotional state. It uses an emotion engine to determine the required skills based on the sensed emotional state. Finally, it provides an individually optimized training program to recommend training based on the determined skills.

[1381] Explanation of program processing

[1382] The server provides a user interface for entering information such as the user's job title, background, and characteristics. This interface can be accessed through a web browser or a mobile application. Once the user enters the information, the server stores the information in a database and uses machine learning algorithms (e.g., RandomForestClassifier) ​​to determine the required skills.

[1383] Next, the device uses a camera and microphone to sense the user's emotional state. An emotion recognition library (e.g., EmotionRecognizer) is used to analyze the user's emotional state in real time. Based on the sensed emotional state, the server uses an emotion engine to reassess the required skills and identify appropriate skills.

[1384] Finally, based on the skills determined, the server recommends an individually optimized training program for the user, customized according to the user's job type, background, and emotional state.

[1385] Specific examples

[1386] For example, if this system is installed on a robot working in a factory, the robot will input the worker's job type and career information to determine the required skills. Furthermore, the robot will use a camera and microphone to sense the worker's emotional state and reassess the required skills using an emotion engine. Finally, the robot will provide each worker with an individually optimized training program.

[1387] Prompt Sentence Examples

[1388] User information: "Engineer, 5 years experience"

[1389] Emotion data: "Anger 0.2, Happiness 0.8"

[1390] Using this prompt as input, the system determines the skills required and recommends an appropriate training program.

[1391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1392] Step 1:

[1393] The user inputs information such as occupation, career history, characteristics, etc.

[1394] Input: Information such as user's occupation, career, characteristics, etc.

[1395] Output: User information entered

[1396] Specific operation: A user enters information such as their occupation, career, and characteristics into a form provided through a web browser or mobile application. The entered information is sent to the server.

[1397] Step 2:

[1398] The server stores the entered information in a database.

[1399] Input: User information entered

[1400] Output: User information stored in the database

[1401] Specific operation: The server stores the received user information in a database, which allows it to retain the information needed for subsequent processing.

[1402] Step 3:

[1403] The server uses machine learning algorithms to determine the required skills.

[1404] Input: User information stored in the database

[1405] Output: Determined required skills

[1406] Specific operation: The server retrieves user information from the database and uses a machine learning algorithm (e.g., RandomForestClassifier) ​​to determine the skills required by the user. The determined skills are temporarily stored.

[1407] Step 4:

[1408] The device senses the user's emotional state.

[1409] Input: Real-time video and audio data of the user

[1410] Output: Sensed emotional state data

[1411] Specific operation: The device uses a camera and microphone to capture real-time video and audio of the user, and analyzes the user's emotional state using an emotion recognition library (e.g., EmotionRecognizer). The detected emotional state data is sent to the server.

[1412] Step 5:

[1413] The server reassess the required skills based on the sensed emotional state.

[1414] Input: Sensed emotional state data, determined required skills

[1415] Output: Reassessed required skills

[1416] Specific operation: The server receives the sensed emotional state data and re-evaluates the required skills using the emotion engine, thereby identifying the appropriate skills according to the user's emotional state.

[1417] Step 6:

[1418] The server recommends training based on the determined skills.

[1419] Input: Reassessed required skills

[1420] Output: Recommended training program

[1421] Specific operation: The server generates and recommends an individually optimized training program for the user based on the reassessed required skills. The recommended training program is notified to the user.

[1422] Example 3

[1423] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1424] Conventional training recommendation systems can determine the necessary skills based on the user's occupation and career history and recommend training, but they have the problem of not being able to adjust training based on the user's emotional state. This makes it difficult to provide optimal training when the user is feeling stressed or in a particular emotional state.

[1425] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for inputting information such as the user's occupation, career history, and characteristics, a means for determining the required skills based on the input information, a means for recommending training based on the determined skills, a means for recognizing the user's emotional state, and a means for adjusting the content of the training based on the recognized emotional state. This makes it possible to provide optimal training that takes into account both the user's skills and emotional state.

[1426] "User" refers to any individual or entity that uses the System.

[1427] "Job type" refers to the type of work or job a user is engaged in.

[1428] "Career" refers to a user's past work experience, educational background, qualifications, etc.

[1429] "Traits" refer to individual attributes such as a user's skills, interests, personality, etc.

[1430] "Means for inputting information" refers to an interface for inputting information such as the user's occupation, career, characteristics, etc. into the system.

[1431] "Means for determining required skills" refers to algorithms or programs that identify the skills a user needs based on input information.

[1432] "Training recommendation means" refers to a function that suggests appropriate training to users based on their assessed skills.

[1433] "Means for recognizing emotional states" refers to technology that analyzes a user's facial expressions and voice data to identify their emotions.

[1434] "Means for adjusting training content" refers to a function for changing the difficulty or content of training based on the recognized emotional state.

[1435] This invention is a system that inputs information such as a user's occupation, career history, and characteristics, determines the required skills based on that information, and further adjusts the training content by recognizing the user's emotional state. A specific embodiment of this system will be described below.

[1436] 1. Collecting user skill data

[1437] The device collects information such as the user's occupation, career history, and characteristics. This information can be obtained from the self-evaluation sheet the user fills out and past training history. For example, the device collects the user's history of taking "Basic Programming Course" and "Intermediate Data Analysis Course."

[1438] 2. Skills data analysis

[1439] The server analyzes the collected skill data. Specifically, it uses Python data analysis libraries (e.g., pandas, scikit-learn) to organize the data and evaluate the user's skill level. For example, it can confirm that a user has taken the "Basic Programming Course" but has not yet taken the "Intermediate Data Analysis Course."

[1440] 3. Training Recommendations

[1441] The server recommends the most suitable training for the user based on the analysis results. The server identifies the user's skill gaps and lists training courses to fill those gaps. For example, if the user has not taken the "Intermediate Data Analysis Course," the server recommends that the user take the "Intermediate Data Analysis Course."

[1442] 4. Collecting user sentiment data

[1443] The device collects facial and voice data from the user. Specifically, it uses a webcam and microphone to capture the user's facial expressions and voice in real time, for example, collecting facial expressions and vocalizations made by the user during training.

[1444] 5. Emotional Data Analysis

[1445] The server analyzes the collected emotional data, specifically using OpenCV and TensorFlow to analyze the user's facial and voice data to identify their emotional state, for example, whether they are feeling stressed or happy.

[1446] 6. Adjust your training based on emotions

[1447] The server adjusts the training content based on the analysis results of the emotion engine. Specifically, it changes the difficulty and content of the training depending on the user's emotional state. For example, if the user is feeling stressed, it will recommend relaxation and stress management training.

[1448] Examples and prompts

[1449] Skills recommended:

[1450] Example: If it is determined that User A needs programming skills, the server will recommend training courses such as "Basic Programming Course" and "Intermediate Data Analysis Course."

[1451] Emotion-based recommendations:

[1452] Example: If User B is identified as feeling stressed, the server may recommend relaxation and stress management training.

[1453] Prompt Sentence Examples

[1454] Skills recommended:

[1455] "Recommend optimal programming training based on the user's past activity history and self-assessment data."

[1456] Emotion-based recommendations:

[1457] "Analyze the user's facial and voice data and recommend relaxation training if they are feeling stressed."

[1458] In this way, the system can provide optimal training based on the user's skills and emotional state. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1459] Step 1: Collect user skill data

[1460] The terminal collects information such as the user's occupation, career, and characteristics.

[1461] Input: Self-evaluation sheets and past training history entered by users.

[1462] Specific operation: The terminal retrieves the self-evaluation sheet filled out by the user and the history of past training sessions from the database.

[1463] Output: Collected information about the user's occupation, background, characteristics, etc.

[1464] Step 2: Analyze your skills data

[1465] The server analyzes the collected skill data.

[1466] Input: Collected information about the user, such as their occupation, background, and characteristics.

[1467] How it works: The server organizes the data using Python's pandas library and uses scikit-learn to evaluate the user's skill level using a machine learning model.

[1468] Output: Analysis of the user's skill level.

[1469] Step 3: Training Recommendations

[1470] The server then recommends the most suitable training for the user based on the analysis results.

[1471] Input: Analysis results about the user's skill level.

[1472] What it does: The server identifies the user's skill gaps and lists training courses to fill those gaps.

[1473] Output: A list of recommended training courses.

[1474] Step 4: Collect user sentiment data

[1475] The device collects the user's facial expressions and voice data.

[1476] Input: User's facial expressions and voice.

[1477] What it does: The device uses a webcam and microphone to capture the user's facial expressions and voice in real time.

[1478] Output: Collected facial and voice data of the user.

[1479] Step 5: Analyze the sentiment data

[1480] The server analyzes the collected emotion data.

[1481] Input: Collected facial and voice data of the user.

[1482] How it works: The server uses OpenCV and TensorFlow to analyze the user's facial expression and voice data to identify their emotional state.

[1483] Output: Analysis results about the user's emotional state.

[1484] Step 6: Adjust your training based on emotions

[1485] The server adjusts the training content based on the analysis results of the emotion engine.

[1486] Input: Analysis results about the user's emotional state.

[1487] Specific operation: The server changes the difficulty and content of the training depending on the user's emotional state.

[1488] Output: Adjusted training content.

[1489] In this way, the system can provide optimal training based on the user's skills and emotional state.

[1490] (Application example 3)

[1491] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1492] Conventional training recommendation systems determine the necessary skills based on the user's job type and career history, and recommend training, but they are unable to take the user's emotional state into account. This makes it difficult to provide optimal training when the user is stressed or in a specific emotional state. Furthermore, since they are unable to evaluate skills or recommend training in real time, they lack immediacy. To solve these issues, a system that recognizes the user's emotional state and adjusts training accordingly is needed.

[1493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1494] In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, means for acquiring video in real time, means for processing the acquired video, means for evaluating skills based on the processed video, means for recommending training based on the evaluation results, and means for displaying the recommended training. This makes it possible to provide optimal training according to the user's emotional state, and to evaluate skills and recommend training in real time.

[1495] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to an interface that allows the user to provide information about their occupation, past experience, and personal characteristics to the system.

[1496] The "means for determining the required skills based on the input information" is an algorithm or program that analyzes the information provided by the user and identifies the skills required by that user.

[1497] The "means for recommending training based on determined skills" is a function for suggesting training programs suitable for the user based on the identified skills.

[1498] "Means for recognizing user emotions" refers to technology for detecting and analyzing a user's emotional state from facial expressions, voice, etc.

[1499] "Means for adjusting training recommendations based on recognized emotions" is a function for changing training content and methods depending on the user's emotional state.

[1500] "Means for acquiring video in real time" refers to technology for capturing video of a user in real time using a device such as a camera.

[1501] The "means for processing the captured video" refers to an algorithm or program for analyzing the captured video data and extracting the necessary information.

[1502] The "means for evaluating skills based on processed video" is a function for evaluating a user's skill level based on analyzed video data.

[1503] "Means for recommending training based on evaluation results" is a function for suggesting training programs suitable for users based on the results of skill evaluation.

[1504] The "means for displaying recommended training" refers to a display or interface for visually presenting the recommended training content to the user.

[1505] As an embodiment of the present invention, a system will be described in which employees in a brick-and-mortar store wear smart glasses and receive real-time skill assessments and training recommendations.

[1506] System configuration

[1507] This system uses the following hardware and software:

[1508] Hardware: Smart glasses, camera

[1509] Software: OpenCV (image processing library), emotion_recognition (emotion recognition library), skill_assessment (skill assessment library), training_recommendation (training recommendation library)

[1510] Program processing

[1511] The server includes means for inputting information such as the user's occupation, career, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, means for acquiring video in real time, means for processing the acquired video, means for evaluating skills based on the processed video, means for recommending training based on the evaluation results, and means for displaying the recommended training.

[1512] Processing flow

[1513] 1. Entering user information: The user puts on the smart glasses and enters information such as occupation, career, and characteristics into the system.

[1514] 2. Skill determination: The server analyzes the input information and determines the skills required by the user.

[1515] 3. Emotion recognition: The camera captures the user's video in real time and uses an emotion recognition library to recognize the user's emotional state.

[1516] 4. Skill evaluation: Process the captured video and evaluate the user's skill level using the skill evaluation library.

[1517] 5. Training recommendation: Based on the results of skill assessment and emotion recognition, the training recommendation library recommends the most suitable training for the user.

[1518] 6. Training display: Recommended training content will be displayed on the smart glasses display.

[1519] Specific examples

[1520] For example, if a brick-and-mortar employee is feeling stressed while serving a customer, the emotion engine will recognize this and recommend training on stress management, while if the employee is feeling happy, it will recommend training on maintaining that emotion.

[1521] Prompt Sentence Examples

[1522] "What training would you recommend if an employee is experiencing stress while serving customers?"

[1523] In this way, store employees can efficiently improve their skills and receive optimal training according to their emotional state.

[1524] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1525] Step 1:

[1526] The user puts on the smart glasses and inputs information such as occupation, career, characteristics, etc.

[1527] Input: Information such as user's occupation, career, characteristics, etc.

[1528] Output: User information entered

[1529] Specific Actions: A user uses the smart glasses interface to input information about their job title, past experience, and personal characteristics.

[1530] Step 2:

[1531] The server analyzes the input information and determines the required skills.

[1532] Input: User information

[1533] Output: Required skills

[1534] Specific operation: The server analyzes the information entered by the user and uses a skill evaluation algorithm to identify the skills required by the user.

[1535] Step 3:

[1536] A camera is used to capture images of the user in real time.

[1537] Input: None (real-time video acquisition)

[1538] Output: Captured video data

[1539] Specific operation: The camera installed in the smart glasses captures the user's image in real time and transmits the image data to the server.

[1540] Step 4:

[1541] The server processes the captured video and recognizes the user's emotions.

[1542] Input: Captured video data

[1543] Output: Perceived emotional state

[1544] Specific operation: The server uses an emotion recognition library to analyze the user's facial expressions and voice from the captured video data and recognize their emotional state.

[1545] Step 5:

[1546] The server performs skill evaluation based on the processed video.

[1547] Input: Processed video data

[1548] Output: Skill evaluation results

[1549] Specific operation: The server uses the skill assessment library to analyze the processed video data and assess the user's skill level.

[1550] Step 6:

[1551] The server recommends training based on the results of skill evaluation and emotion recognition.

[1552] Input: Skill assessment results, perceived emotional state

[1553] Output: Recommended training content

[1554] Specific operation: The server uses the training recommendation library to integrate the skill evaluation results and emotion recognition results and generate the optimal training program for the user.

[1555] Step 7:

[1556] Recommended training content is displayed on the smart glasses display.

[1557] Input: Recommended training content

[1558] Output: Training content displayed on the smart glasses display

[1559] Specific operation: The server sends the recommended training content to the smart glasses and displays it on the screen, and the user follows the training.

[1560] 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.

[1561] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1562] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1563] 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.

[1564] [Third embodiment]

[1565] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1566] 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.

[1567] 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).

[1568] 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.

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1577] "Example 1"

[1578] As one embodiment of the present invention, a system is provided that has an interface for users to input information such as their occupation, career history, characteristics, etc. This interface is designed to allow users to easily input information using text boxes, drop-down menus, etc.

[1579] "Example 2"

[1580] Next, a function is provided to determine the required skills based on the input information. This function uses machine learning algorithms and rule-based logic to determine the required skills from the user's information. For example, if the user is an engineer, it will determine that programming skills and project management skills are required.

[1581] "Example 3"

[1582] Finally, a training recommendation function will be provided based on the skills identified. This function recommends training that is individually optimized for each user. For example, if it is determined that a user needs programming skills, it will recommend training in Python or Java. This allows users to efficiently acquire the skills they need.

[1583] The processing flow of each embodiment will be described below.

[1584] "Example 1"

[1585] Step 1: The user enters information such as their job title, career history, and characteristics through the system interface. For example, the user enters information such as "Engineer," "5 years of experience," and "Java skills" into text boxes and drop-down menus.

[1586] "Example 2"

[1587] Step 1: The system takes the input and determines the required skills. This determination is made using machine learning algorithms and rule-based logic. For example, if a user inputs that they are an "engineer" and have "Java skills," the system might determine that they need skills such as "project management" and "database administration."

[1588] "Example 3"

[1589] Step 1: The system recommends training based on the skills identified. This recommendation is tailored to the individual user. For example, if the system determines that the skill "project management" is needed, it will recommend project management training.

[1590] Step 2: The user receives recommendations from the system and acquires the necessary skills by taking the recommended training. This allows the user to efficiently acquire the skills they need.

[1591] Example 1

[1592] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1593] In conventional systems, even if users input information such as their occupation, career history, and characteristics, it was difficult to effectively utilize that information to provide individually optimized career advice. In addition, there was a lack of a way to automatically determine the skills users needed and provide appropriate training and advice. This meant that users were unable to obtain specific guidance to effectively advance their career path.

[1594] 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.

[1595] In this invention, the server includes means for inputting information such as a user's occupation, career history, and characteristics, means for saving the input information in a database, means for processing and analyzing the saved information, means for generating prompt sentences based on the processed and analyzed information, means for sending the generated prompt sentences to a generative AI model, and means for providing the user with a response from the generative AI model, thereby enabling the user to receive individually optimized career advice based on their own information.

[1596] "User" refers to an individual who uses the system to input information such as their occupation, career history, and characteristics, and receives career advice.

[1597] "Occupation" refers to the type of work or duties that a user is engaged in.

[1598] "Career" refers to the job and work history a user has had.

[1599] "Features" refers to individual information such as a user's skills, expertise, and characteristics.

[1600] "Input means" refers to the interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[1601] "Database" refers to an information system for storing and managing information entered by users.

[1602] "Means for processing and analyzing" refers to the function of processing the information stored in the database and normalizing and filtering the data as necessary.

[1603] A "prompt sentence" refers to an instruction sentence for a generative AI model that is generated based on user input information.

[1604] A "generative AI model" refers to an artificial intelligence model that generates a response based on a prompt sentence based on user input information.

[1605] "Response" refers to the career advice or suggestions that the generative AI model generates based on the prompt.

[1606] "Means to provide" refers to the functionality for communicating responses from the generative AI model to the user.

[1607] This invention is a system that provides individually optimized career advice to users by inputting information such as their occupation, career history, and characteristics, and utilizing a generative AI model based on that information. Specific embodiments of this system are described below.

[1608] Hardware and software used

[1609] Hardware:

[1610] Terminal: The device through which a user enters information (e.g., PC, smartphone, tablet)

[1611] Server: A server for storing data, processing and analyzing it, and communicating with the generative AI model

[1612] software:

[1613] Database: A database system (e.g., MySQL, PostgreSQL) for storing user input information.

[1614] Generative AI model: An artificial intelligence model that generates responses based on user input (e.g., GPT-4).

[1615] Program processing

[1616] Users use their devices to access the system's interface and enter information such as their job title, career history, characteristics, etc. This interface includes UI components such as text boxes and drop-down menus, and is designed to allow users to easily enter information.

[1617] The terminal sends the information entered by the user to the server as an HTTP request, and the server stores the received information in a database and performs validation to maintain data integrity.

[1618] The server then processes and analyzes the stored information. Specifically, it standardizes the entered job titles and characteristics, and normalizes and filters the data as necessary. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[1619] The server generates a prompt based on the processed and parsed information. For example, it generates the following prompt:

[1620] "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Could you please give me some career advice based on this information?"

[1621] The generated prompt is sent from the server to the generative AI model, which then generates an appropriate response based on the received prompt. For example, it might generate a response like, "Based on your experience and skill set, we recommend you move into cloud computing or data science as your next step."

[1622] Finally, the server sends the generated response to the device, where the user receives it, and receives specific career advice based on their job type and background.

[1623] Specific examples

[1624] For example, if a user enters the job title as "Software Engineer," experience of "5 years," and characteristics of "Python, Java," the following specific actions will occur:

[1625] 1. User enters "Software Engineer", "5 years", "Python, Java".

[1626] 2. The device sends this information to the server.

[1627] 3. The server stores the information in a database.

[1628] 4. The server normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[1629] 5. The server generates the prompt: "I'm a software engineer with 5 years of experience, primarily working with Python and Java. Based on this information, please give me some career advice that suits me."

[1630] 6. The server sends this prompt to the generative AI model.

[1631] 7. The generative AI model generates a response saying, "Based on your experience and skill set, we recommend your next steps be in cloud computing or data science."

[1632] 8. The server sends this response to the device.

[1633] 9. The user receives this response and gets advice on their next career step.

[1634] In this way, users can receive specific career advice based on their own information.

[1635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1636] Step 1:

[1637] The user accesses the interface.

[1638] Input: A user accesses the system's interface using a terminal.

[1639] Output: The interface is displayed, ready for the user to enter information.

[1640] What happens: The user launches a web browser or mobile app and opens a system URL or app.

[1641] Step 2:

[1642] The user inputs information such as occupation, career history, characteristics, etc.

[1643] Input: Users enter information such as job title, experience, and characteristics into text boxes and drop-down menus.

[1644] Output: The input information is temporarily stored on the device.

[1645] Specific operation: The user enters information such as "Software Engineer", "5 years", "Python, Java" into the input fields.

[1646] Step 3:

[1647] The terminal sends the input information to the server.

[1648] Input: Information entered by the user.

[1649] Output: The input information is sent to the server as an HTTP request.

[1650] Specific operation: The terminal generates an HTTP request including the information entered by the user and sends it to the server.

[1651] Step 4:

[1652] The server stores the received information in a database.

[1653] Input: User input information sent from the device.

[1654] Output: The information is saved in a database.

[1655] Specific operation: The server stores the received information in a database (e.g., MySQL, PostgreSQL) and performs validation to maintain data integrity.

[1656] Step 5:

[1657] The server processes and analyzes the stored information.

[1658] Input: User information stored in the database.

[1659] Output: Processed and analyzed information.

[1660] Specific operation: The server analyzes the stored information and standardizes job titles and characteristics. For example, it normalizes "Software Engineer" to "Software Engineer" and analyzes "Python, Java" as a skill set.

[1661] Step 6:

[1662] The server generates a prompt based on the processed and parsed information.

[1663] Input: Processed and analyzed user information.

[1664] Output: The generated prompt statement.

[1665] Specific behavior: Based on the user's information, the server generates a prompt statement: "I am a software engineer with 5 years of experience. I mainly use Python and Java. Based on this information, please provide me with suitable career advice."

[1666] Step 7:

[1667] The server sends the generated prompt to the generative AI model.

[1668] Input: The generated prompt statement.

[1669] Output: The prompt sent to the generative AI model.

[1670] Specific operation: The server sends the generated prompt sentence to a generative AI model (e.g., GPT-4).

[1671] Step 8:

[1672] A generative AI model generates a response.

[1673] Input: The prompt sent to the generative AI model.

[1674] Output: The generated response.

[1675] How it works: The generative AI model generates an appropriate response based on the prompt it receives, such as, "Based on your experience and skill set, I would recommend your next step be in cloud computing or data science."

[1676] Step 9:

[1677] The server sends the generated response to the terminal.

[1678] Input: The response from the generative AI model.

[1679] Output: The response sent to the terminal.

[1680] Specific operation: The server sends the response received from the generative AI model to the terminal.

[1681] Step 10:

[1682] The user receives the response.

[1683] Input: The response sent to the terminal.

[1684] Output: The response received by the user.

[1685] Specific operation: The user checks the response from the generative AI model through the device and receives advice on the next career step.

[1686] (Application example 1)

[1687] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1688] Conventional job information systems were unable to fully utilize information such as the user's job type, career history, and characteristics, making it difficult to provide users with the most suitable job information. Furthermore, they lacked the functionality to automatically determine the skills required by users and recommend appropriate training. This resulted in insufficient support for users to effectively advance their careers.

[1689] 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.

[1690] In this invention, the server includes means for inputting information such as the user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for generating optimal job information based on the input information, and means for providing job information using a generative AI model. This makes it possible to provide job information and training that are individually optimized for each user.

[1691] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc.

[1692] The "means for determining the required skills based on the input information" refers to a device or software that analyzes the information input by the user and automatically determines the skills the user needs based on that information.

[1693] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[1694] The "means for generating optimal job information based on input information" refers to a device or software that generates optimal job information for a user based on information input by the user.

[1695] A "means for providing job information using a generative AI model" is a device or software that uses a generative AI model to provide job information to users.

[1696] The system for implementing this invention provides an interface for users to input information such as their occupation, career history, characteristics, etc. The interface is implemented as a smartphone application and is designed to allow users to easily input information using text boxes and drop-down menus.

[1697] The server receives the information entered by the user and performs data analysis to determine the required skills. This analysis is performed using a Python program and OpenAI API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3) based on information such as the user's occupation, background, and characteristics.

[1698] The server then recommends appropriate training to the user based on the determined skills, providing training programs optimized for the user's skill level and job type.

[1699] The server then generates optimal job listings based on the information entered by the user. Using a generative AI model, the server provides optimal job listings to the user. In this process, the server sends the user's job type, career history, characteristics, and other information to the generative AI model as prompts to generate optimal job listings.

[1700] As a concrete example, consider the case where a user enters the following information:

[1701] Job title: Software Engineer

[1702] Experience: 5 years of development experience

[1703] Specialties: Team leader experience

[1704] In this case, the prompt sent to the generative AI model would be:

[1705] Job title: Software Engineer

[1706] Experience: 5 years of development experience

[1707] Specialties: Team leader experience

[1708] Please provide the best job information based on this.

[1709] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[1710] This system is realized using a smartphone, a server, and a generative AI model (e.g., GPT-3). The smartphone provides the user interface, and the server analyzes data and calls the generative AI model. The generative AI model generates optimal job listings based on user input.

[1711] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1712] Step 1:

[1713] A user uses a smartphone device to input information such as occupation, career history, and characteristics. The input interface includes text boxes and drop-down menus, and is designed to allow users to easily input information. The input information is then sent from the device to a server.

[1714] Input: Information such as user's occupation, career, characteristics, etc.

[1715] Output: User information sent to the server

[1716] Step 2:

[1717] The server analyzes the received user information and determines the required skills. This analysis is performed using a Python program and OpenAI API. Specifically, the required skills are automatically determined using a generative AI model (e.g., GPT-3) based on information such as the user's occupation, career history, and characteristics.

[1718] Input: User information

[1719] Output: List of required skills

[1720] Step 3:

[1721] The server recommends appropriate training to the user based on the determined skills, the recommended training being optimized for the user's skill level and job type.

[1722] Input: List of required skills

[1723] Output: Recommended training program

[1724] Step 4:

[1725] The server generates optimal job information based on the information entered by the user. The generative AI model is used to provide optimal job information to the user. In this process, information such as the user's job type, career history, and characteristics is sent to the generative AI model as a prompt sentence, which generates optimal job information.

[1726] Input: User information

[1727] Output: Generated job listings

[1728] Step 5:

[1729] The server then sends the generated job information to the user's smartphone, allowing the user to check the most suitable job information on the smartphone.

[1730] Input: Generated job posting

[1731] Output: Job information displayed on the user's smartphone

[1732] As a specific example of operation, consider the case where the user inputs the following information:

[1733] Job title: Software Engineer

[1734] Experience: 5 years of development experience

[1735] Specialties: Team leader experience

[1736] In this case, the prompt sent to the generative AI model would be:

[1737] Job title: Software Engineer

[1738] Experience: 5 years of development experience

[1739] Specialties: Team leader experience

[1740] Please provide the best job information based on this.

[1741] The server sends this prompt to the generative AI model and provides the returned job information to the user, allowing the user to obtain job information optimized for their job type, background, and characteristics.

[1742] Example 2

[1743] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1744] Previous systems had the problem that it was difficult for users to identify the skills they needed based on their job type and career history, and there was a lack of information to receive appropriate training.In addition, there was a lack of means to recommend individually optimized training to users, which led to the issue of users not being able to improve their skills efficiently.

[1745] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for receiving and preprocessing the input information, a means for determining the required skills based on the preprocessed information, and a means for returning the determined skills to the user. This enables the user to automatically identify the required skills based on their occupation and career, and receive individually optimized training.

[1746] "User" means any individual or entity that uses the System.

[1747] "Occupation" is information that indicates the type of occupation the user is engaged in.

[1748] "Career" refers to historical information such as the user's past work experience and educational background.

[1749] "Characteristics" are a user's skills, characteristics, and other individual information.

[1750] The "means for inputting" is an interface that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[1751] "Means for receiving" refers to the function by which the server receives information sent by the user.

[1752] "Preprocessing" is the process of converting received information into a format that can be understood by a machine learning model.

[1753] The "means of determining" is a function that identifies the required skills based on preprocessed information.

[1754] "Means of return" is a function that notifies the user of the determined skill.

[1755] A "machine learning algorithm" is a computational method for learning patterns from data and making predictions and classifications.

[1756] "Rule-based logic" is a method of making decisions based on predefined rules.

[1757] "Training" is the process of educating and training users to acquire the necessary skills.

[1758] "Individually optimized training" means an education or training program that is customized to a user's specific needs and circumstances.

[1759] MODE FOR CARRYING OUT THE INVENTION

[1760] This invention is a system that allows users to identify the skills they need based on their job type and career history, and receive individually optimized training. This system is implemented using the following hardware and software.

[1761] Hardware and software used

[1762] Hardware: A server with a powerful CPU and sufficient memory

[1763] Software: Python, TensorFlow (machine learning library), Scikit-learn (machine learning library), Flask (web framework)

[1764] System Overview

[1765] 1. Enter your user information:

[1766] The user uses the terminal to input information such as his / her occupation, career history, characteristics, etc.

[1767] Example: User types "Engineer."

[1768] 2. Data reception and preprocessing:

[1769] The server receives the information sent by the user.

[1770] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[1771] 3. Skill Assessment:

[1772] The server inputs the preprocessed data into the machine learning model.

[1773] It uses machine learning models (using TensorFlow and Scikit-learn) and rule-based logic to determine the skills a user needs.

[1774] Example: If the user types "engineer," the model determines that "programming skills" and "project management skills" are also required.

[1775] 4. Returning the results:

[1776] The server returns the determined skill to the user.

[1777] For example, it says, "You need programming and project management skills."

[1778] Examples and prompts

[1779] Examples:

[1780] If a user types in "data scientist," the system will determine that "data analysis skills" and "machine learning skills" are required.

[1781] If a user types in "marketing manager," the system will determine that "digital marketing skills" and "data analysis skills" are required.

[1782] Example prompt sentence:

[1783] "Please enter your occupation: e.g., Engineer, Data Scientist, Marketer"

[1784] "Please tell us your current skills: e.g., Python, project management, data analysis"

[1785] The system automatically determines the required skills by having users input information about their job type and skills, and provides them to users, allowing them to clearly understand the skills required for their career path.

[1786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1787] Step 1:

[1788] The user uses the terminal to enter information such as their occupation, career history, and characteristics.

[1789] Specifically, the user opens a browser on the device, accesses the system's web page, enters "engineer" in the occupation input field, and clicks the submit button.

[1790] Input: User's occupation, career, characteristics, etc.

[1791] Output: The information entered by the user is sent to the server

[1792] Step 2:

[1793] The server receives the information sent by the user.

[1794] Specifically, the server receives an HTTP request and obtains the user's input data.

[1795] Input: User-submitted information

[1796] Output: User information received by the server

[1797] Step 3:

[1798] The server preprocesses the received data and converts it into a format that the machine learning model can understand.

[1799] Specifically, the server tokenizes the text data and converts it into numeric data as needed.

[1800] Input: User information received by the server

[1801] Output: Preprocessed data

[1802] Step 4:

[1803] The server inputs the preprocessed data into the machine learning model.

[1804] Specifically, the server uses TensorFlow and Scikit-learn to input data into the model.

[1805] Input: Preprocessed data

[1806] Output: Skill assessment results from the machine learning model

[1807] Step 5:

[1808] The server uses machine learning models and rule-based logic to determine the skills a user needs.

[1809] Specifically, the model outputs "programming skills" and "project management skills" in response to the input "engineer."

[1810] Input: Preprocessed data

[1811] Output: List of required skills

[1812] Step 6:

[1813] The server returns the determined skill to the user.

[1814] Specifically, the server generates an HTTP response that tells the user, "You need programming skills and project management skills."

[1815] Input: List of required skills

[1816] Output: Skill information returned to the user

[1817] In this way, users can identify the skills they need based on their job type and background and receive individually optimized training.

[1818] (Application example 2)

[1819] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1820] While conventional systems can determine the required skills based on the user's job type and career history and recommend training, they have the problem of not being able to assign optimal tasks to factory workers. There was also a need for a system that could assign optimal tasks based on workers' years of experience, qualifications, and past performance evaluations.

[1821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1822] In this invention, the server includes means for inputting information such as the user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information on workers in the factory and assigning optimal tasks. This makes it possible not only to recommend individually optimized training to the user, but also to assign optimal tasks to workers in the factory.

[1823] "User" refers to an individual or organization that uses the System.

[1824] "Job type" refers to the type of work or job a user is engaged in.

[1825] "Career" refers to a user's past work experience, educational background, qualifications, etc.

[1826] "Characteristics" refers to a user's individual characteristics, such as skills, abilities, and personality.

[1827] "Means for inputting information" refers to a method or device for inputting information such as a user's occupation, career, characteristics, etc. into the system.

[1828] "Means for determining required skills" refers to a method or device for identifying the skills required by a user based on input information.

[1829] "Training recommendation means" refers to a method or device for suggesting appropriate training to a user based on the determined skills.

[1830] "Factory workers" refers to employees working in a factory.

[1831] "Worker information" refers to data such as years of experience, qualifications, and past performance evaluations of workers in the factory.

[1832] The "means for assigning optimal work" refers to a method or device for determining and assigning the most suitable work to each worker based on information about the worker.

[1833] A system for implementing this invention includes means for inputting information such as a user's job type, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, and means for inputting information about workers in a factory and assigning them the most appropriate work.

[1834] System configuration

[1835] The system consists of the following main components:

[1836] 1. User information input terminal: A terminal for inputting information such as the user's occupation, career, characteristics, etc. For example, a PC or tablet is used.

[1837] 2. Server: Processes the input information, determines the skills needed, recommends training, and processes information on workers in the factory to assign them the most appropriate tasks.

[1838] 3. Database: A database for storing user information, worker information, skill information, training information, etc.

[1839] 4. Machine learning algorithm: An algorithm to determine the required skills based on user information and assign optimal tasks. For example, RandomForestClassifier from the scikit-learn library is used.

[1840] Processing flow

[1841] 1. Input of user information: The user inputs information such as occupation, career, characteristics, etc. into the system through an input terminal.

[1842] 2. Information processing: The server stores the input information in a database and uses machine learning algorithms to determine the required skills.

[1843] 3. Training Recommendation: The server recommends the most appropriate training for the user based on the determined skills.

[1844] 4. Entering worker information: Enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals.

[1845] 5. Work allocation: The server allocates the optimal work based on the information of the workers.

[1846] Hardware and software used

[1847] Hardware: PCs, tablets, servers, database servers

[1848] Software: pandas library, scikit-learn library, database management system (e.g., MySQL)

[1849] Specific examples

[1850] For example, when a new worker joins a factory, the worker's years of experience, qualifications, and past performance evaluations are entered into the system via an input terminal. Based on this information, the server uses machine learning algorithms to assign the most suitable tasks.

[1851] Example prompt sentence:

[1852] Enter the new worker's information. Enter the number of years of experience, the number of certifications, and past performance ratings. For example, if the worker has 5 years of experience, 3 certifications, and a past performance rating of 4, enter the following:

[1853] Years of experience: 5

[1854] Number of qualifications: 3

[1855] Past performance rating: 4

[1856] In this way, the system can not only recommend individually optimized training for users, but also assign optimal tasks to workers within the factory.

[1857] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1858] Step 1:

[1859] Users input information such as their occupation, career, and characteristics into the system through an input terminal. The input information is sent to the server and stored in a database. The input data includes the user's occupation, career, and characteristics.

[1860] Step 2:

[1861] The server retrieves user information stored in a database and uses a machine learning algorithm to determine the required skills. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the required skills based on the user's occupation and background. The input data is user information, and the output data is the required skills.

[1862] Step 3:

[1863] The server recommends the most suitable training for the user based on the determined skills. The recommended training information is sent to the user's device. The input data is the required skills, and the output data is the recommended training.

[1864] Step 4:

[1865] Users enter information about workers in the factory (years of experience, qualifications, past performance evaluations, etc.) into the system through input terminals. The entered information is sent to the server and stored in the database. The input data includes the worker's years of experience, qualifications, past performance evaluations, etc.

[1866] Step 5:

[1867] The server retrieves worker information stored in a database and uses a machine learning algorithm to assign the optimal task. Specifically, it uses the RandomForestClassifier from the scikit-learn library to predict the optimal task based on the worker's years of experience, qualifications, and past performance evaluations. The input data is worker information, and the output data is the optimal task.

[1868] Step 6:

[1869] The server sends the assigned work information to the worker's terminal. The worker checks and executes the assigned work through the terminal. The input data is the optimal work, and the output data is the work information displayed on the worker's terminal.

[1870] Example 3

[1871] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1872] With conventional skill assessment systems, it was difficult to provide individually optimized training programs when determining the skills required based on the user's job type and career history. Furthermore, because user skill assessment was performed manually, there was a problem of reduced accuracy and efficiency. This made it difficult for users to efficiently acquire the skills they needed.

[1873] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1874] In this invention, the server includes a means for inputting information such as the user's occupation, career, and characteristics, a means for transmitting the input information to the server, a means for the server to evaluate the user's skills using a generative AI model, a means for generating a training program based on the evaluation results, and a means for providing the generated training program to the user. This makes it possible to automatically evaluate the user's skills and provide an individually optimized training program.

[1875] "User" means an individual or organization that uses the System to receive a skill assessment or training program recommendation.

[1876] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[1877] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[1878] "Characteristics" refers to information that refers to a user's individual characteristics, such as skills, abilities, interests, and personality.

[1879] "Means for input" refers to the interface or device that allows the user to provide information such as occupation, career, characteristics, etc. to the system.

[1880] "Means for sending" refers to the communication means for sending the information entered by the user to the server.

[1881] "Server" refers to a computer system that receives and processes information sent by users.

[1882] A "generative AI model" refers to an artificial intelligence model that analyzes user information and performs skill evaluations and generates training programs.

[1883] "Means of evaluation" refers to the process or algorithm used to evaluate a user's skills using a generative AI model.

[1884] "Training Program" refers to the content of education and training that enables users to acquire the skills they need.

[1885] "Means for providing" refers to an interface or device for presenting the generated training program to the user.

[1886] This invention is a system that inputs information such as a user's occupation, career history, and characteristics, evaluates the required skills based on that information, and recommends the most appropriate training program. Specific embodiments of this system are described below.

[1887] First, the user enters information such as their occupation, career history, and characteristics into the device. The device then sends the entered information to the server. At this time, the device converts the information into JSON format and transmits it securely using the HTTPS protocol.

[1888] The server analyzes the received information and evaluates the user's skill using a generative AI model, such as OpenAI's GPT-4. The server inputs the following prompt into the generative AI model:

[1889] Assess users' skills and recommend the best training program for them. User input includes:

[1890] "I have basic programming skills, but I lack data science knowledge."

[1891] The generative AI model analyzes the prompt and evaluates the user's skills. Based on the evaluation results, the server generates the optimal training program for the user, for example, recommending a Python data analysis course to learn the basics of data science.

[1892] The server sends the generated training program to the terminal. The data is also encrypted when sent. The terminal displays the received training program to the user. The user can check the displayed training program and take it if necessary.

[1893] This system allows users to efficiently evaluate their skills and receive individually optimized training programs. This allows users to efficiently acquire the necessary skills. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1894] Step 1:

[1895] The user inputs information such as occupation, career history, characteristics, etc.

[1896] The user enters information such as their occupation, career history, and characteristics into the input form on the device. For example, they might enter, "I have basic programming skills, but I lack knowledge of data science." The entered information is temporarily stored in the device's memory.

[1897] Step 2:

[1898] The terminal transmits the input information to the server.

[1899] The terminal converts the information entered by the user into JSON format and sends it to the server using the HTTPS protocol. The input data is a JSON object containing information such as job type, career history, and characteristics. The transmitted data is received by the server's receiving module.

[1900] Step 3:

[1901] The server evaluates the user's skills using a generative AI model.

[1902] The server parses the received JSON data and inputs a prompt sentence to the generative AI model. The prompt sentence is generated as follows:

[1903] Assess users' skills and recommend the best training program for them. User input includes:

[1904] "I have basic programming skills, but I lack data science knowledge."

[1905] The generative AI model analyzes the prompt and evaluates the user's skills, outputting the results as data including the skills and level required by the user.

[1906] Step 4:

[1907] The server generates a training program based on the evaluation results.

[1908] The server searches the database for an appropriate training program based on the evaluation results obtained from the generative AI model. For example, it may select a Python data analysis course to learn the basics of data science. The generated training program is output as a JSON object containing content optimized for the user.

[1909] Step 5:

[1910] The server transmits the generated training program to the terminal.

[1911] The server converts the generated training program into JSON format and sends it to the device using the HTTPS protocol. The transmitted data is received by the device's receiving module.

[1912] Step 6:

[1913] The terminal displays the training program to the user.

[1914] The device parses the received JSON data and displays the contents of the training program to the user. The user can check the displayed training program and click the "Take Part" button if necessary. The displayed content includes details of the training and how to take part.

[1915] (Application example 3)

[1916] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1917] Conventional skill assessment systems can determine the skills required based on a user's job type and career history and recommend training, but they have the problem of not being able to assess skills in real time or recommend training based on that assessment. This makes it difficult for users to acquire the skills they actually need in a timely manner, and makes it difficult to improve their skills efficiently.

[1918] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results. This makes it possible to evaluate the user's skills in real time and recommend optimal training based on the evaluation.

[1919] "User" refers to any individual or entity that uses the System.

[1920] "Job type" refers to the type of work or role a user has.

[1921] "Career" refers to a user's past work experience, educational background, and other history.

[1922] "Traits" refer to the individual attributes of a user, such as their skills, abilities, interests, and personality.

[1923] "Means for inputting information" refers to an interface or device for inputting information such as the user's occupation, career, characteristics, etc. into the system.

[1924] "Means for determining required skills" refers to algorithms or programs that identify the skills a user needs based on input information.

[1925] "Training recommendation means" refers to a function that suggests appropriate training to users based on their assessed skills.

[1926] "Real-time assessment means" refers to sensors, data analysis algorithms, or programs that assess a user's skills in real time.

[1927] "Means for recommending training based on evaluation results" refers to a function for suggesting optimal training to users based on skill evaluation results obtained in real time.

[1928] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for evaluating the user's skills in real time, and means for recommending training based on the evaluation results.

[1929] System program generation

[1930] The server provides an interface for users to input information such as their occupation, career, and characteristics. Users input this information using smart glasses or a smartphone. The input information is sent to the server and stored in a database.

[1931] The server then runs an algorithm to determine the required skills based on the input information. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills.

[1932] Furthermore, the server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors in the smart glasses monitor the user's movements and send the data to the server, which then analyzes the data and evaluates the user's skill level.

[1933] Based on the evaluation results, the server will recommend optimal training to the user. For example, if the robot is not operating smoothly, the server will display a message on the smart glasses saying, "Robot operation training is recommended."

[1934] Hardware and software used

[1935] Hardware: smart glasses, smartphone, server

[1936] Software: Database management system, data analysis algorithms, user interface

[1937] Specific examples

[1938] Consider a scenario in which a factory robot operator is wearing smart glasses. When the operator starts operating the robot, the smart glasses monitor the operation and evaluate the operator's skills. For example, if the operator is not operating the robot smoothly, the smart glasses will display a message saying, "Robot operation training is recommended."

[1939] Prompt Sentence Examples

[1940] Develop an application that evaluates a user's skills and recommends necessary training. The user is a factory robot operator and wears smart glasses. Skill evaluation is performed in real time, and training recommendations are made based on the evaluation results. Specific skills include robot operation, maintenance, and programming. Training recommendations should be made for skills with an evaluation score of less than 5.

[1941] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1942] Step 1:

[1943] Users use smart glasses or smartphones to input information such as their occupation, career history, and characteristics. The input information is sent from the device to a server, which then stores the information in a database. The input data includes the user's occupation (e.g., factory robot operator), career history (e.g., 5 years of experience), and characteristics (e.g., programming skills).

[1944] Step 2:

[1945] The server runs an algorithm that determines the required skills based on the information stored in the database. The algorithm identifies which skills are required based on the user's job type and background. For example, a factory robot operator might need robot operation and maintenance skills. The input data is the user information, and the output data is a list of required skills.

[1946] Step 3:

[1947] The server uses sensors and data analysis algorithms to evaluate the user's skills in real time. The sensors installed in the smart glasses monitor the user's movements and send the data to the server. The server analyzes this data and evaluates the user's skill level. The input data is sensor data, and the output data is the skill evaluation result.

[1948] Step 4:

[1949] The server recommends optimal training to the user based on the evaluation results. For example, if the user's robot operation is not smooth, the server displays a message on the smart glasses saying, "We recommend training to operate the robot." The input data is the skill evaluation result, and the output data is a training recommendation message.

[1950] Step 5:

[1951] The user receives training recommendation messages from the server and starts the necessary training. The user reports the training progress to the server via smart glasses or a smartphone. The server stores the training progress in a database and monitors the improvement of the user's skill level. The input data is the training progress, and the output data is the updated skill evaluation results.

[1952] 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.

[1953] "Example 1"

[1954] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[1955] "Example 2"

[1956] The emotion engine also determines the skills required based on the user's emotions. For example, if the emotion engine recognizes that the user is feeling angry, it determines that communication skills and problem-solving skills are required. This makes it possible to acquire skills according to the user's emotional state.

[1957] "Example 3"

[1958] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes the user's emotions and tailors workout recommendations based on those emotions. Specifically, if the emotion engine recognizes that the user is feeling stressed, it recommends relaxation and stress management workouts. Alternatively, if the emotion engine recognizes that the user is feeling joy, it recommends workouts to maintain that joy. This provides optimal workouts based on the user's emotional state.

[1959] The processing flow of each embodiment will be described below.

[1960] "Example 1"

[1961] Step 1: The emotion engine recognizes the user's emotion.

[1962] Step 2: Adjust training recommendations based on the emotions recognized by the emotion engine.

[1963] Step 3: For example, if the emotion engine recognizes that the user is feeling stressed, it will recommend relaxation and stress management training.

[1964] Step 4: If the emotion engine also detects that the user is experiencing joy, it will recommend training to maintain that joy.

[1965] "Example 2"

[1966] Step 1: The emotion engine recognizes the user's emotion.

[1967] Step 2: Determine the skills required based on the emotions recognized by the emotion engine.

[1968] Step 3: For example, if the emotion engine detects that the user is angry, it determines that communication skills and problem-solving skills are needed.

[1969] Example 1

[1970] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1971] Conventional training recommendation systems can determine the necessary skills based on the user's job type and career history and recommend training, but they do not take into account the user's emotional state when recommending training. As a result, they are unable to provide optimal training according to the user's emotional state, which can lead to reduced user satisfaction and effectiveness.

[1972] 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.

[1973] In this invention, the server includes means for inputting information such as the user's occupation, career, and characteristics, means for receiving the input information and storing it in a database, means for analyzing the user's emotions based on the stored information, and means for recommending training based on the analyzed emotions, thereby making it possible to recommend optimal training according to the user's emotional state.

[1974] "User" refers to an individual or organization who uses the system to input information such as their occupation, career history, and characteristics, and receives training recommendations.

[1975] "Occupation" is information that indicates the type of work or role that a user is engaged in.

[1976] "Career" refers to information that refers to a user's past work experience, educational background, qualifications, and other history.

[1977] "Characteristics" refers to information that refers to a user's personal characteristics, skills, interests, etc.

[1978] "Input means" refers to an interface that allows a user to provide information such as their occupation, career, characteristics, etc. to the system.

[1979] "Database" refers to a storage device or system for organizing and storing information entered by users.

[1980] "Means of emotion analysis" refers to technologies and algorithms used to recognize and analyze emotions from user input.

[1981] "Training recommendation means" refers to a function for suggesting suitable training to a user based on the analyzed emotions.

[1982] "Natural language processing technology" refers to technology for analyzing meaning and emotions from text data entered by users.

[1983] MODE FOR CARRYING OUT THE INVENTION

[1984] This invention is a system that allows a user to input information such as their occupation, career, and characteristics, analyzes emotions based on that information, and recommends optimal training. Specific embodiments of this system are described below.

[1985] 1. Program Generation

[1986] The server provides an interface for the user to enter information. This interface includes text boxes and drop-down menus designed to make it easy for the user to enter information.

[1987] 2. Program Processing

[1988] The server receives the information entered by the user and stores it in a database, which organizes and stores information such as the user's occupation, career, and characteristics.

[1989] The server then uses an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing (NLP) technology to analyze emotions from the user's input. Specifically, it uses "natural language processing technology" to extract emotions from the user's text.

[1990] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user enters "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend relaxation and stress management training.

[1991] 3. Specific Examples

[1992] As a specific example, consider the case where a user inputs "I've been busy at work lately and I'm feeling stressed."

[1993] 1. Using the device interface, the user types "I've been busy and stressed out lately" into a text box.

[1994] 2. The server receives this information and stores it in a database as "Occupation: Office Worker, Emotion: Stress."

[1995] 3. The server uses an emotion engine (e.g., natural language processing technology) to analyze the emotion "stress."

[1996] 4. Based on the analysis results, the server recommends "relaxation and stress management training."

[1997] 5. Users undergo recommended training and practice specific methods to reduce stress (e.g., deep breathing, meditation).

[1998] Prompt Sentence Examples

[1999] If a user types, "I've been busy and stressed lately at work," how would the emotion engine respond and what training would it recommend?

[2000] In this way, an optimal workout is provided depending on the user's emotional state.

[2001] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2002] Step 1:

[2003] The user enters information

[2004] The user uses the terminal interface to input information such as their occupation, career, and characteristics. The input information is sent to the server through text boxes and drop-down menus. The input data includes text information such as occupation, career, and characteristics.

[2005] Step 2:

[2006] The server receives the information and stores it in a database

[2007] The server receives the information sent by the user and stores it in a database. Specifically, it analyzes the received text data and stores it in the appropriate fields. For example, it may be stored in a format such as "Occupation: Company employee," "Career: 5 years of experience," or "Characteristics: Low stress tolerance." The input data is text information, and the output data is structured data stored in the database.

[2008] Step 3:

[2009] The server uses an emotion engine to analyze the user's emotions.

[2010] The server starts an emotion engine based on the stored information. The emotion engine uses natural language processing technology to analyze emotions from the user's input. For example, natural language processing technology can be used to extract emotions such as "stress" or "joy" from the user's text. The input data is text information, and the output data is analyzed emotional information.

[2011] Step 4:

[2012] The server recommends training based on the analysis results.

[2013] After the emotion engine analyzes the user's emotions, the server recommends training based on the results. For example, if a user inputs "I've been busy at work lately and I'm feeling stressed," the emotion engine will recognize "stress" and the server will recommend training related to relaxation and stress management. The input data is the analyzed emotion information, and the output data is the recommended training content.

[2014] Step 5:

[2015] Users receive recommended training

[2016] The user receives training recommendations from the server. The training content is optimized according to the user's emotional state, providing specific methods for reducing stress and maintaining joy. For example, relaxation techniques such as deep breathing and meditation are recommended. The input data is the recommended training content, and the output data is the user's practice results.

[2017] (Application example 1)

[2018] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2019] While conventional systems can determine the necessary skills based on a user's job type and career history and recommend training, they have the problem of not being able to adjust training or workload taking into account the user's emotional state, making it difficult to provide optimal training and work environments that take into account the user's stress and emotional state.

[2020] 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.

[2021] In this invention, the server includes means for inputting information such as the user's occupation, career history, and characteristics, means for determining the required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting the recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state. This makes it possible to provide optimal training and a working environment according to the user's emotional state.

[2022] "Means for inputting information such as the user's occupation, career, characteristics, etc." refers to a device or software that provides an interface for the user to input information such as their occupation, career, characteristics, etc. into the system.

[2023] "Means for determining required skills based on input information" refers to a device or software that analyzes information such as job type, career history, and characteristics input by the user and automatically determines the skills the user requires.

[2024] A "means for recommending training based on determined skills" is a device or software that recommends appropriate training to a user based on determined skills.

[2025] "Means for recognizing user emotions" refers to a device or software that analyzes the user's facial expressions, voice, actions, etc., and recognizes the user's emotional state.

[2026] A "means for adjusting training recommendations based on recognized emotions" is a device or software that adjusts the content or method of training based on the recognized emotional state of the user.

[2027] The "means for adjusting the workload according to the user's emotional state" refers to a device or software that adjusts the pace or workload of work according to the user's emotional state.

[2028] A system for implementing this invention includes means for inputting information such as a user's occupation, career history, characteristics, etc., means for determining required skills based on the input information, means for recommending training based on the determined skills, means for recognizing the user's emotions, means for adjusting recommended training based on the recognized emotions, and means for adjusting the workload according to the user's emotional state.

[2029] Generating a Program

[2030] The program of this system is configured as follows:

[2031] 1. Enter your user information:

[2032] The server provides an interface for users to enter information about their job title, background, characteristics, etc. This may include text boxes and drop-down menus.

[2033] 2. Determine the skills needed:

[2034] The server analyzes the input information and automatically determines the skills the user needs, using machine learning algorithms and database searches.

[2035] 3. Training Recommendations:

[2036] ...

Claims

1. A means for inputting user job type, career, and characteristic information; A means for determining the skills required by the user based on the input information; means for recommending training based on said determined skills; A means for generating job information using a generative AI model based on a prompt sentence combining the input information and providing the job information to the user; A system including:

2. A means for inputting user job type, career, and characteristic information; A means for determining the skills required by the user based on the input information; means for recommending training based on said determined skills; means for recognizing an emotional state of the user; a means for adjusting the training recommendation using a generative AI model based on a prompt sentence that combines the input information and the recognized emotional state; means for adjusting the user's workload based on a prompt sentence combining the input information and the recognized emotional state; A system including:

3. A means for inputting user job type, career, and characteristic information; A means for determining the skills required by the user based on the input information; means for recommending training based on said determined skills; means for recognizing an emotional state of the user; A means for acquiring video in real time; A means for processing the acquired image; A means for performing a skill evaluation based on the processed video; a means for adjusting the training recommendation using a generative AI model based on a prompt sentence that combines the input information, the recognized emotional state, and the result of the skill assessment; means for displaying the recommended workouts; A system including:

Images